The world of Fortune 500 companies is often portrayed as a realm of success, innovation, and prosperity. However, behind the glossy exterior, executives sometimes grapple with hiring nightmares that can turn their corporate dreams into a waking reality. In this blog, we’ll delve into some real-life stories that highlight the challenges and tribulations faced by executives in the high-stakes world of Fortune 500 hiring.

The Case of the Impersonator: One executive from a renowned technology giant found themselves in a baffling situation when they discovered that a newly hired senior manager was not who they claimed to be. The imposter had fabricated an impressive resume and managed to deceive the interview panel with ease. The fallout from this revelation not only damaged the company’s reputation but also raised questions about the effectiveness of its hiring processes.

Lesson Learned: Rigorous background checks are crucial, even for high-profile positions.

The Overqualified Underperformer: In another Fortune 500 company, an executive made the mistake of hiring a candidate who appeared overqualified on paper but struggled to perform in their actual role. Despite an impressive track record, the new hire seemed disinterested in the day-to-day responsibilities of the job, leading to a significant loss in productivity and morale among the team.

Lesson Learned: Fit for the role and company culture is as important as qualifications.

The Cultural Mismatch: Fortune 500 companies often emphasize the importance of corporate culture. However, one executive learned the hard way when they hired a candidate whose values and working style clashed with the established culture. The resulting tension and disruption had a ripple effect, causing discontent among other team members and affecting overall productivity.

Lesson Learned: Prioritize cultural fit to avoid disrupting team dynamics.

The Vanishing Act: Imagine hiring a top-level executive only to have them disappear without a trace shortly after onboarding. This nightmare scenario became a reality for one Fortune 500 company. The executive, who was expected to lead a crucial division, vanished without fulfilling any of their responsibilities, leaving the company in a leadership vacuum.

Lesson Learned: Establish clear expectations and communication channels from the beginning.

The Social Media Debacle: A well-regarded executive faced a public relations nightmare when an employee’s offensive social media posts came to light. Despite a thorough hiring process, the company had failed to discover the individual’s controversial online presence, leading to a damaging scandal that required immediate damage control.

Lesson Learned: Monitor candidates’ online presence and social media activities.

Conclusion

These real-life hiring nightmares from Fortune 500 companies serve as cautionary tales for executives navigating the challenging terrain of talent acquisition. From identity deception to cultural mismatches, these stories highlight the importance of robust hiring processes, thorough background checks, and a keen focus on cultural fit. As the business landscape continues to evolve, so must the strategies executives employ to ensure they don’t fall victim to the hiring horrors that lurk in the shadows of corporate success.

In the ever-evolving world of software architecture, one term has generated a significant amount of “hoopla” in recent years — microservices.

Every time I talk to a budding developer and entrepreneur — there is one thing in common. Everyone wants their software to be written using a microservices architecture. A slight deep-dive on why they want so reveals that it helps in building a distributed system and scaling to millions of users.

Seriously? Man, you haven’t yet figured out the PMF and you are talking about a million-user scale. Even if so, is it the solution? You might kill me for this but I don’t think so!

In this blog, we’ll explore the origins of microservices, delve into code snippets, look at companies successfully leveraging this architecture, discuss cases where it may not be the best fit, weigh the pros and cons, and ultimately help you decide if microservices are the right choice for your product and team.

The Origins of Microservices

Microservices are an evolution of the service-oriented architecture (SOA) principles, and they’ve gained prominence in the era of cloud computing and DevOps. The concept of microservices is rooted in breaking down complex applications into smaller, independent services that can be developed, deployed, and scaled separately. This approach allows for better flexibility, scalability, and maintenance of software systems.

Code Snippets: A Glimpse into Microservices

# Order Service
@app.route(“/create_order”, methods=[“POST”])
def create_order():
# Business logic to create an order
return “Order created successfully”

# Inventory Service
@app.route(“/update_inventory”, methods=[“POST”])
def update_inventory():
# Business logic to update inventory
return “Inventory updated”

# Payment Service
@app.route(“/process_payment”, methods=[“POST”])
def process_payment():
# Business logic to process payment
return “Payment processed successfully”

In this example, we have three microservices (Order, Inventory, and Payment) that communicate via HTTP requests. Each service focuses on a specific business capability, and they can be developed and deployed independently.

Companies Embracing Microservices — Netflix, Uber, Amazon

Companies Using Monolith — Microsoft, Oracle, Salesforce

When to Go for Microservices

The decision to embrace microservices should be driven by your project’s requirements. Consider microservices when:

• You anticipate rapid growth and need scalable solutions.
• Your team is experienced in managing distributed systems.
• You require independent development, testing, and deployment of components.
• Your application has clearly defined, isolated functionalities.

Time to Build for Microservices

1. Initial Development: Building a software system using a microservices architecture often requires more time upfront compared to a monolithic approach. This is because you’re essentially developing multiple small, independent services, each with its own codebase, APIs, and infrastructure. This initial overhead can extend the time it takes to deliver a minimum viable product (MVP).
2. Team Coordination: Microservices often involve multiple development teams, each responsible for a specific service. Coordinating the work of these teams, especially in larger projects, can be time-consuming. Effective communication and collaboration are crucial to ensure that each service aligns with the overall project goals.
3. Deployment and Orchestration: Microservices demand more sophisticated deployment and orchestration mechanisms. Containerization technologies like Docker and container orchestration platforms like Kubernetes are often used, which can require additional setup and configuration. However, once set up, they facilitate easier and more frequent deployments.
4. Testing and Integration: Testing in a microservices architecture is inherently more complex due to the distributed nature of the system. Ensuring that all services work together seamlessly may take more time, and a robust testing strategy is essential to catch issues early.
5. Iterative Development: Over time, as the project evolves, microservices can offer faster development cycles. Individual services can be updated or extended independently, which can lead to quicker feature development and release.
6. Scaling: Microservices allow for fine-grained scalability, meaning you can scale specific services as needed. This can lead to faster response times and better performance when compared to a monolithic system that scales as a whole.

Time to Build for Monolith

1. Initial Development: Building a monolithic application typically requires less upfront effort because everything is contained within a single codebase. You can quickly create a functional prototype and get started with development.
2. Simplicity: Monoliths are simpler to develop, test, and deploy initially. There’s no need for the complex setup of microservices infrastructure, which can speed up development time.
3. Single Development Team: Monolithic applications can be developed by a single team, which simplifies coordination and communication. Team members can have a shared understanding of the entire codebase.
4. Deployment: Deploying a monolithic application is straightforward as there’s only one unit to deploy. This simplicity can lead to faster release cycles in the early stages.
5. Testing: Testing a monolithic application is more straightforward, as all components are tightly integrated. This can result in quicker identification and resolution of issues during development.
6. Scaling: While monolithic applications can be scaled horizontally by replicating the entire application, the process may not be as fine-grained or efficient as microservices.

Choosing Between Microservices and Monolith

The decision on whether to opt for microservices or a monolithic architecture should be influenced by your project’s specific needs and constraints. If rapid development and time-to-market are your primary goals, a monolithic architecture might be more suitable initially. On the other hand, if you anticipate long-term scalability and agility, and are willing to invest time in setup and coordination, microservices could be the way to go.

It’s important to note that some organizations choose a hybrid approach, starting with a monolithic architecture for simplicity and transitioning to microservices as the project evolves and the need for scalability and flexibility becomes more pressing. This allows them to balance the advantages of both approaches while managing the associated time and complexity.

Let’s talk more on this? Here is the link to my calendly — https://calendly.com/sukantk/talk

In a rapidly changing financial landscape, traditional credit scoring methods can be limiting when it comes to serving underserved communities in tier 3 cities and villages. Enter the innovative approach of leveraging social media data to evaluate the creditworthiness of individuals with little to no financial digital footprint. This blog explores the journey of one of the biggest banks in Indonesia, where they tapped into the world of Instagram and TikTok to bridge the credit gap.

Challenges: A Blank Digital Slate

In the vast landscapes of tier 3 cities and villages, individuals often lack a substantial financial digital footprint. This scarcity of traditional data makes it challenging for banks to assess the creditworthiness of potential customers. Without a credit history or any substantial records, how can financial institutions make informed lending decisions?

Opportunities: The Rise of Social Media

While these individuals might have minimal financial data, they are far from being digitally inactive. Many of them are highly active on social media platforms like Instagram and TikTok. This digital activity opened up a new world of possibilities for assessing creditworthiness.

The Solution: Leveraging Social Media Data

The Bank decided to explore this untapped resource by requesting access to the Instagram and TikTok accounts of potential customers through API-based access. This initiative marked a significant shift in the way credit scoring is traditionally done. Instead of relying solely on financial history, the bank began to analyze users’ interactions on these platforms.

Mapping Users: Low, Medium, High Categories

The bank developed a robust algorithm that evaluated users based on several factors:

1. Interaction with Users with Valid Credit Scores: Users who engaged with individuals known to have good credit histories were assigned higher scores.
2. Engagement on Social Media Platforms: The frequency and level of engagement on Instagram and TikTok were analyzed to gauge the users’ level of social activity and connectedness.
3. Content Consumption: The type of content consumed also played a significant role. Users engaging with content related to financial literacy and responsible financial behaviour were considered positively.

Accuracy Achieved: 70%

Through this innovative approach, The Bank was able to accurately categorize potential customers into low, medium, and high-risk groups with a remarkable 70% accuracy rate. This was a groundbreaking achievement, given the lack of traditional credit data.

Dynamic Credit Scoring

What sets this approach apart is its adaptability. The Bank didn’t stop at the initial categorization. Instead, they continued to monitor users’ social media activities, allowing them to adjust ratings and categories accordingly. This dynamic approach ensured that customers’ evolving financial behaviours were reflected in their credit scores.

Pseudo Code for Social Media-Based Credit Scoring

Let’s break down the pseudo-code for the social media-based credit scoring system:# Import necessary libraries
import requests
import social_media_analyzer

# Define user’s social media accounts
instagram_username = “user123”
tiktok_username = “user456”

# Get API-based access to social media data
instagram_data = requests.get(f”https://instagram-api.com/{instagram_username}”)
tiktok_data = requests.get(f”https://tiktok-api.com/{tiktok_username}”)

# Analyze social media data
social_media_score = social_media_analyzer.analyze(instagram_data, tiktok_data)

# Evaluate user’s creditworthiness
if social_media_score > 0.7:
credit_category = “High”
elif social_media_score > 0.4:
credit_category = “Medium”
else:
credit_category = “Low”

# Update user’s credit rating and category in the database
database.update_credit_rating(user_id, credit_category)

# Continuously monitor and adjust credit scores over time
while True:
new_social_media_data = requests.get_updated_data(instagram_data, tiktok_data)
updated_social_media_score = social_media_analyzer.analyze(new_social_media_data)
if updated_social_media_score != social_media_score:
database.update_credit_rating(user_id, updated_social_media_score)
social_media_score = updated_social_media_score

This pseudocode outlines a simplified version of the process. In practice, the algorithm would be more complex and involve extensive data analysis.

Conclusion: A Bright Future for Inclusive Banking

This Bank’s pioneering approach to credit scoring has demonstrated that the world of social media can be a powerful ally in extending financial services to underserved communities. As technology continues to advance, financial institutions worldwide may look to leverage alternative data sources, like social media, to create more inclusive and accurate credit scoring models. This innovative approach has the potential to reshape the landscape of banking, making financial services accessible to a broader range of individuals, regardless of their traditional credit history.

In today’s data-driven world, enterprises often find themselves grappling with vast amounts of data — terabytes, petabytes, and beyond. While the growth of data is exciting, it also poses unique challenges, particularly when it comes to data migration. Moving such large volumes of data from one environment to another can be a complex, daunting task, often perceived as a “pain.” This blog will delve into the intricacies of enterprise data migration, showcasing our expertise in the field, and explore how AWS services, with their scalability and flexibility, can be harnessed to overcome these challenges.

The Importance of Data Migration

Data migration is a fundamental process for modern enterprises. Whether it’s adopting new cloud services, upgrading legacy systems, or simply restructuring data storage, there are several reasons why data migration is critical:

• Optimizing Performance: As data accumulates, systems can become sluggish. Data migration is often a way to reorganize and enhance performance.
• Cost-Efficiency: Migrating data to the cloud or more cost-effective storage solutions can lead to significant cost savings.
• Scalability: With growing data, scaling infrastructure as needed becomes paramount.
• Compliance: Enterprises need to ensure data is stored and managed in compliance with industry regulations.

Challenges in Enterprise Data Migration

Data Volume and Complexity

Enterprises dealing with terabytes of data face a mountainous challenge. Data volumes can make the migration process slow and resource-intensive. Data is often not neatly organized, making it complex to transfer and store efficiently.

Downtime and Business Continuity

Downtime is the enemy of productivity. Migrating data without interrupting business operations is a juggling act that requires careful planning and execution.

Data Security and Compliance

Protecting data and ensuring compliance during the migration is a critical concern. Security breaches or compliance violations can have severe consequences.

How AWS Services Can Help

Amazon Web Services (AWS) offers a suite of services and tools designed to tackle these challenges head-on.

AWS Data Migration Services

AWS provides various data migration services, including AWS Database Migration Service (DMS), AWS Snowball, and AWS DataSync. These tools can be tailored to your specific migration needs, whether it’s moving databases, data lakes, or large volumes of data via physical devices.

AWS Snowball and Snowmobile

For especially massive data volumes, AWS Snowball and Snowmobile are ingenious solutions. These physical devices are shipped to your location, enabling you to transfer terabytes or petabytes of data quickly and securely.

AWS Glue and ETL

AWS Glue is a fully managed ETL service that makes it easy to move data between data stores, transforming data as needed. This helps resolve data format discrepancies and ensures data integrity.

Overcoming the Challenges

Migrating terabytes of data becomes less painful when you have the right strategies in place:

• Thorough Planning: Detailed planning is essential. This includes understanding data dependencies, data mapping, and identifying critical systems.
• Incremental Migration: Rather than migrating everything at once, consider incremental migrations. This minimizes downtime and provides more control over the process.
• Data Validation: Rigorous data validation and verification are crucial. Ensuring data integrity before, during, and after migration is vital.
• Testing in Staging Environments: Before migration, thorough testing in a staging environment is a must. This helps identify and address issues before they impact the production environment.

Wrapping It Up

Data migration in enterprise products is indeed a challenging endeavour, especially when dealing with terabytes of data. However, it can be a manageable and even transformational process with the right expertise, tools, and strategies. AWS services offer robust solutions to tackle these challenges and unlock the benefits of scalable, secure, and cost-effective data management.

As data migration experts, we understand the nuances of this critical process. We are here to help you navigate the complexities and ensure a smooth transition for your enterprise, no matter how vast your data may be. If you’re looking to embark on your data migration journey or need guidance, reach out to us today. Your enterprise’s data future awaits!

Let’s answer the very fundamental question — Why is there a need to migrate from Microsoft SQL Server

In sum: MS SQL Server isn’t a data warehouse

By separating your data warehouse from your database, you also minimize the risk of anything happening to your real-time business data.

What are the challenges when planning a migration?

1. 1. SQL Compatibility: Translating SQL Server queries and stored procedures to Redshift’s SQL dialect can be challenging due to differences in syntax, functions, and supported features. You may need to rewrite and optimize your SQL code.

1. 1. Query Optimization: Ensuring that your SQL queries are optimized for Redshift’s distributed architecture is crucial. Understanding Redshift’s query performance considerations and making appropriate adjustments can be challenging.

1. 1. ETL Scripting: Developing and maintaining complex ETL scripts to transform and load data from SQL Server to Redshift can be challenging. It requires expertise in data transformation, data validation, and error handling.

1. 1. Data Type Mapping: Accurately mapping data types from SQL Server to Redshift and handling data type conversions and compatibility issues can be complex. Mismatches can lead to data corruption or performance problems.

1. 
   
   1. Data Validation and Verification: Writing code to validate and verify data integrity during and after migration is important but can be challenging due to the complexity of data transformations.

High Level Data Migration Architecture from SQL Server to Redshift

 

Methods to Migrate SQL Server to Redshift

Migrating data from SQL Server to Redshift is a crucial step for businesses that want to take advantage of cloud-based data warehousing. Fortunately, there are multiple ways to migrate SQL Server to Redshift, each with its own set of advantages and disadvantages. In this guide, we will focus on the most popular and easy methods to migrate your data from SQL Server to Redshift.

Method 1: Using the AWS Database Migration Service (DMS)

Method 2: Using custom ETL scripting

Method 3: Using SaaS Alternatives

For the sake of simplicity and avoiding any context switching we will only talk about Method 1 in this blog!

Method 1: Using the AWS Database Migration Service (DMS)

AWS Database Migration Service (DMS) is a fully managed service that enables you to migrate data from one database to another. You can use DMS to migrate data from SQL Server to Redshift quickly and efficiently.

 

Here’s an overview of the process of migrating data from SQL Server to Redshift using AWS Database Migration Service:

Set up an AWS DMS replication instance: First, you need to create a replication instance within AWS DMS. A replication instance is a server instance that acts as a communication channel between SQL Server and Redshift. This replication instance is responsible for reading data from the SQL Server, applying any necessary transformations, and writing the data to the Redshift.

Create source and target endpoints: Once the replication instance is up and running, you’ll need to create endpoints to connect to your SQL Server source database and Redshift target database.

Configure replication settings: AWS DMS provides a number of settings that allow you to fine-tune the replication process to meet your specific needs. You’ll need to configure these settings to ensure that the migration process goes smoothly.

Start the replication process: Once everything is configured correctly, you can start the replication process. AWS DMS will begin copying data from your SQL Server source database to your Redshift target database.

Monitor the migration: During the migration process, it’s important to monitor and ensure that everything is running smoothly. AWS DMS provides several tools to help you monitor the migration process, including cloud watch logs and metrics.

Verify the data: Once the migration is complete, verifying that all data was successfully migrated is important. You should perform a thorough test to ensure that everything is working as expected.

The process of migrating data from SQL Server to Redshift using AWS Database Migration Service (DMS) is relatively straightforward and can be completed in a matter of hours or days, depending on the size of your data set.

For detailed steps to migrating data using AWS Database Migration Service (DMS), please refer to the official AWS documentation.

Pros of using DMS:

• • DMS is a fully managed service, so you don’t need to worry about managing the infrastructure or software.

• • DMS supports one-time and ongoing migrations to migrate your data to Redshift at your own pace.

• • DMS can replicate data changes in real time, so you can keep your Redshift database up-to-date with your SQL Server database.

• • DMS supports heterogeneous migrations so that you can migrate data from different database platforms to Redshift.

Cons of using DMS:

• • DMS only supports a subset of SQL Server features. So some advanced features such as SQL Server Agent jobs, change data capture, FILESTREAM, and Full-Text Search are not supported.

• • DMS can be complex to set up and configure, especially for complex migrations with many source and target endpoints.

In the world of technology, there are average engineers, and then there are the legendary 10x engineers. The latter, often considered the unicorns of the industry, can deliver results equivalent to the work of ten ordinary developers. Their stories are nothing short of awe-inspiring, and in this blog, we will uncover what sets them apart.

The concept of the 10x software developer dates back to “Exploratory experimental studies comparing online and offline programming performance”, (Sackman, Erickson, and Grant), a research paper published in Communications of the ACM in January 1968.

Comparing a 10x engineer and an average engineer is like comparing Picasso with street painters!

The 10x Engineer: A Force of Nature

A 10x engineer is not merely a term; it’s a testament to exceptional skill and efficiency. Here’s what differentiates them from the rest:

Do 10x engineers deliver the work of 10 Devs?

The term “10x engineer” signifies their incredible productivity. These extraordinary individuals have the unique ability to deliver results that would typically require the combined effort of ten developers. But that doesn’t make them superhuman!

10x just signifies their attention to detail and their extraordinary ownership!

Efficiency Leads to Usual Work Hours

Their efficiency is their superpower. This efficiency typically comes from the microscopic planning these guys are super obsessed with! While an average engineer might need long hours because of poor planning to complete a task, a 10x engineer accomplishes the same with grace, often working regular hours and maintaining a healthy work-life balance.

Analytical Skills and Logical Thinking

They possess an analytical prowess that’s second to none. Their logical thinking allows them to dissect complex problems, identify elegant solutions, and execute them flawlessly.

Foreseeing Future Perspectives

10x engineers have the uncanny ability to visualize the future implications of their tasks. They also try to visualise how a certain product or feature can potentially evolve in future! All this because they love what they are doing and they end up attaching their ego to their art! This forward-thinking approach results in solutions that stand the test of time.

Insatiable Curiosity

Curiosity fuels their drive to explore new technologies and tackle challenges head-on. They’re lifelong learners who continually seek to expand their knowledge and skills.

A Product Mindset

These engineers don’t merely write code; they take ownership of the entire product. Their work isn’t about lines of code; it’s about accountability, impact, and excellence.

Let’s drive inspiration from some mind-boggling 10x Engineer stories!

#1 — The Python Maestro

In 2013, at Flipkart, a programmer with a Java background embarked on a remarkable journey. He picked up a critical piece of code and transformed it into Python. This single act of innovation led to substantial performance improvements, changing the game at Flipkart.

#2 — The Warp-Speed Gamers

In 2017, a group of individuals hailing from average colleges took on the colossal task of building a gaming platform. A project that typically spans over a year was completed in just 4–5 months. Their extraordinary efficiency left everyone astounded.

#3 — WhatsApp’s Lean Dream Team

WhatsApp managed its entire tech infrastructure with a modest team of 30–40 engineers before being acquired by Meta. This lean and highly proficient group proved that exceptional results don’t always require an army.

The Hiring Dilemma — Avg Engineer or 10x Engineer

So, what should companies do? Should they hire ten average engineers or invest in one 10x engineer? It’s a question that doesn’t have a one-size-fits-all answer. The decision depends on the project, its scope, and the company’s goals. While the firepower of a 10x engineer can be a game-changer for smaller, high-impact projects, larger teams of skilled individuals might be more appropriate for massive undertakings.

Is the salary also 10x that of average engineers?

It’s no surprise that the salary of a 10x engineer significantly surpasses that of an average engineer. The gap is not arbitrary; it’s a reflection of the extraordinary mindset and contributions of these exceptional individuals. Their work is about quality, efficiency, and results, and companies are willing to pay a premium for that level of excellence.

In conclusion, the journey from an average engineer to a 10x engineer is marked by mindset, skills, and an unyielding commitment to excellence. Their impact, whether through innovation, efficiency, or foresight, is nothing short of awe-inspiring. The technology world thrives on their brilliance, and their stories continue to inspire the next generation of tech enthusiasts and professionals.

On a different note, 😂

Tech careers have been one of the most sought out choices for decades now. Complex problems, scale and impact attract the world’s best minds to this. Tech careers offer dynamic opportunities in a rapidly evolving landscape. From software development and data analysis to cybersecurity and AI research, these roles cater to diverse interests. Constant innovation demands continuous learning, making adaptability crucial. Tech careers promise both lucrative compensation and the chance to shape the future

Why are tech salaries at an all-time high?

Tech salaries are at an all-time high due to several factors –

• The increasing reliance on technology in various industries creates a high demand for skilled professionals.
• Shortages in specialised talent amplify competition among companies, driving up compensation to attract and retain top talent.
• Additionally, tech’s pivotal role in innovation and revenue generation justifies premium pay scales.
• Besides, tech remains the most heavily invested vertical by VCs and PE firms giving the companies access to huge capital. This translates to a constant battle among companies to get the best talent.

Higher salaries are not translating to better engineers!

• While higher salaries in tech can attract top talent, they might not always guarantee better engineers for a few reasons:
• Mismatch of Skills: Higher pay might attract individuals who are motivated primarily by compensation rather than a genuine passion for engineering. This can lead to a mismatch between skills and job requirements.
• Lack of Development: Simply offering a high salary doesn’t inherently foster skill development or innovation. Engineers need opportunities for learning, growth, and challenging projects to become better at what they do.
• Toxic Culture: Some high-paying tech companies might have toxic work cultures, which can hinder collaboration, creativity, and overall engineer improvement.
• Job Hopping: Engineers might switch jobs frequently solely for higher salaries, preventing them from gaining deep expertise and experience in one area.
• Focus on Short-Term Gains: If companies prioritize short-term profit over long-term investment in their engineers, they might not provide the necessary resources and support for skill enhancement.
• Inadequate Training: Offering a high salary doesn’t necessarily mean providing adequate training and mentorship, which are crucial for engineers to develop and excel.
• Lack of Autonomy: If engineers are restricted to specific tasks without autonomy and the ability to contribute ideas, their growth potential can be stifled.
• Burnout: A culture of overwork and burnout can result from the pressure to maintain high levels of productivity, negatively impacting the quality of engineering work.

To foster better engineers, a holistic approach that includes professional development, a positive work environment, challenging projects, and opportunities for creativity is essential, along with competitive compensation.

Demand vs Supply gap

The demand-supply gap in software engineering jobs arises from the exponential growth of technology across industries, generating a substantial need for skilled professionals. However, this demand often outpaces the available pool of qualified candidates. The scarcity of skilled engineers stems from the rapid evolution of technology, creating a mismatch between required skills and outdated educational curricula. This global challenge is further compounded by intense competition for talent among tech hubs and high turnover rates within the industry.

Addressing this gap involves multifaceted efforts. Coding boot camps, upskilling programs, and online learning platforms aim to quickly equip individuals with relevant skills to meet industry needs. Additionally, companies focus on investing in the training and development of current employees and fostering diverse talent pipelines to bridge the divide between demand and supply in the software engineering job market

Why do tech salaries don’t go down despite layoffs?

Tech salaries often don’t go down despite layoffs due to several reasons:

• High Demand for Skilled Talent: The tech industry consistently faces a shortage of skilled professionals, and this demand-supply gap persists even during layoffs. Companies need experienced engineers to maintain and innovate their products, driving competition for top talent and sustaining salary levels.
• Critical Role of Technology: Technology is crucial for businesses to stay competitive and innovative. Layoffs might be a strategic move to optimize operations, but companies still require skilled tech workers to support their core functions.
• Specialized Skills: Many tech roles require specialized knowledge and expertise, making it challenging to replace experienced professionals quickly. As a result, employers are often willing to pay premium salaries to retain and attract skilled engineers.
• Shortage of Qualified Candidates: Layoffs might be temporary for some companies, and when they recover, they need to rebuild their workforce quickly. The limited availability of qualified candidates can lead to bidding wars for top talent, driving salaries up.
• Innovation and Growth: Tech companies understand that investing in their workforce, even during challenging times, is essential for long-term growth and innovation. Competitive salaries help maintain employee morale and attract the best minds in the field.
• Industry Competition: The tech industry is highly competitive, with companies vying for market share and talent. Even during periods of layoffs, companies want to position themselves as attractive employers, which includes offering competitive compensation packages.
• Company Reputation: Maintaining competitive salaries, even during layoffs, helps companies protect their reputation as desirable workplaces. Drastic salary cuts can lead to negative perceptions among potential hires and the industry at large.

In summary, the combination of high demand for skilled professionals, specialized skills, competition for top talent, and the enduring need for innovation in the tech sector collectively contribute to the resilience of tech salaries, even in the face of layoffs.

Salary trend for various tech roles

India vs USA — Salary Trends

Salary growth rate(%) over last 5 years

Salary Benchmarking Report 2023 — India

PS. Want to share something —
Let’s talk; here is my calendly — https://calendly.com/sukantk/talk

-Sukant Kumar