- Backend and full-stack engineers are moving into data engineering roles because their work has a more direct impact on business outcomes and offers better long-term career growth.
- Data engineers have become the most valuable offshore hire because they build the systems that turn raw data into decision-making power (sitting at the core of AI, analytics, and business strategy).
- For companies building offshore teams, this creates urgency: demand is rising fast, competition is increasing, and those who build offshore data teams early (especially in hubs like Bangalore) will have a clear advantage in securing top talent.
There are few things more convincing to prove a sentiment or a trend than seeing it with your own eyes, and that’s exactly what happens when you land in Bangalore’s Terminal 2 and spot the giant Infosys ad promoting data engineering, AI, and GCCs.
When a city like Bangalore, one of the fastest-growing tech hubs in the world, puts up billboards like that, it’s undeniable proof that data engineering has gone ‘mainstream’ and become one of the tech skills of the moment.
Beyond the visual evidence, our leadership team at The Scalers is noticing backend and full-stack engineers retooling their careers for data roles. And companies building or extending their offshore teams in the city are demanding exactly these people.
But why is this happening? And what does it mean for tech leaders setting up offshore teams right now?
The shifting pattern we’re seeing
For years, the most coveted roles in tech sat within “traditional” software domains, and finding talent (as a company) was never too challenging as long as you provided growth opportunities for candidates. Tech stacks, which were the engines of cyclical trends (such as the SaaS boom in the early 2010’s), attracted top engineers and shaped what a successful tech career looked like.
Who doesn’t remember the global appeal of SaaS companies 10 years ago, or even the ongoing appeal of blue-chip IT giants from Silicon Valley? The creation of the Indian IT ecosystem sparked a love affair with the Valley, defining the future of technology and elevating the lives of countless people.
This is ever more true with the ascension of “AI/ML” we’re experiencing. Full-stack engineers and experts in niche areas of traditional development are repurposing their skills to “surf the wave”, so to speak.
In my capacity as a co-founder at the Scalers, I’ve observed a true shift in my network with fresh and existing talent focusing their careers around data/AI/ML roles. This is obvious: just look at all the billboards promoting AI companies such as OpenAI and Perplexity (both of which have created free models to attract Indian users) in the IT corridors; it’s clear we’re already in the early stages of the AI boom.
According to the U.S. Bureau of Labor Statistics, employment for data scientists is projected to grow 34% from 2024 to 2034, far faster than the average for all occupations. While classified separately (we’ll talk about this in the following section), data science roles rely heavily on data engineering infrastructure.
Meanwhile, Robert Half’s 2026 Demand for Skilled Talent report lists data engineering and analytics among the top five fields with the most critical skills gaps among US companies.
Organisations are creating tens of thousands of data roles, demand is skyrocketing, but finding skilled engineers with the skills and vision to adapt to this emerging tech is still challenging.
The three data careers and why (most) engineers pick data engineering
Before we dive into why engineers are making this switch, it’s important to understand the three career paths in the data world, and why data engineering has become the obvious choice for backend and full-stack devs:
- Data science → niche, academic
Data science focuses on modelling and statistical research. It’s powerful, but specialised and less accessible (think PhDs or Physics/Math grads). Fewer roles are available, and the barrier to entry is high. - Data engineering → applied, production-focused
Data engineering focuses on building and scaling data systems, the backbone that enables everything else. Engineers build pipelines, manage infrastructure, and ensure data flows reliably. It has a clearer career path, more open positions, and leverages skills that backend and full-stack developers already have. - Data operations → execution-heavy, lower leverage
Sits one level below data engineering. Involves capturing information, cleaning datasets, researching data quality issues, and automating workflows. It’s a great entry point for juniors, and engineers tend to start here before moving into full data engineering roles.
For engineers coming from traditional backend or full-stack roles, data engineering is the natural fit because it’s where their current skills translate best. The market demand is also strongest here.
Why are engineers really making the switch?
Objectively speaking, this shift reflects where engineers see the most opportunity to leverage their skills. In Bangalore, we’ve been seeing the same pattern: engineers are moving toward roles where their work is more visible, more impactful, and more closely tied to business outcomes.
This is what’s driving the change:
- From output → impact
Data engineering connects directly to decision-making. Engineers are influencing revenue, operations, and strategy. - From features → infrastructure leverage
Instead of shipping isolated features, engineers are building data systems that power entire organisations. - From backend → business-critical systems
Data platforms sit at the core of AI, analytics, and automation, making them central to how companies compete. - From tech-only → cross-industry relevance
Data work isn’t limited to tech anymore. Every industry is becoming data-driven, expanding both opportunity and stability.
In 11 years of working with senior engineers and leaders, I can say with confidence that engineers follow leverage (not hype). Thanks to data, this talent is moving closer to the core of business value creation. And this is the case globally, not just in Bangalore!
What does this mean for companies hiring offshore data engineers?
The role of the data engineer is evolving from pipeline maintenance to owning core data infrastructure and enabling real-time analytics and AI-powered workflows. Tasked with building robust, production-grade data systems that support AI use cases at scale, data engineers are becoming among the most valuable hires for many companies, regardless of size or industry.
If you’re considering building a data team in an offshore location like Bangalore, all I can say is: start soon! The opportunity is significant, but so is the competition. Given the large, competitive talent pool and high demand for roles, the most effective strategy is to enter the market quickly by securing strong senior hires first, and then building the team sustainably around that core.
There’s more data engineering talent availability than ever
According to the State of Data Engineering in India 2025 report, Bangalore has the highest concentration of data engineering professionals, the highest adoption of modern data stacks, and the highest venture capital investment in data-focused startups in the country.
The Bangalore Innovation Report 2025 highlights the city’s vast talent pool, reinforcing its position as a premier global tech hub: it’s home to over 2.5 million software engineers and more than 100,000 PhD holders! Notably, nearly half of all Global Capability Centre (GCC) professionals in Bangalore are in high-end roles, such as engineering, research, and development, the highest concentration among India’s major tech centres.
Furthermore, the city’s workforce is expanding, absorbing between 150,000 and 200,000 new tech employees annually, which accounts for about 45% of national tech hiring. With this rapid influx and the escalating demand for data engineering as a top specialisation, the available talent for data-centric roles is poised for substantial growth.
Lots of demand = Higher competition
As we’ve stated earlier in the article, we’re noticing that companies are going offshore for data teams and data engineering roles more than for traditional developer roles.
This is more evident in companies building distributed teams where engineers are expected to integrate across geographies and support business-critical work. The need is no longer for pipeline builders but for specialists who can work across modern tech stacks and enable decision-making with reliable, well-modelled data.
As demand for data engineers continues to grow, companies across the US, Europe, and APAC are tapping into offshore hubs like Bangalore, often competing for the same talent pool. Although the talent pool for hiring offshore data engineers is quite deep and will continue to grow in the coming years, you must be aware of the challenges you’ll face if you postpone setting up your team, which brings us to our last point.
Entering the market now is a strategic advantage
If you ask us what the best time is to build a data offshore team, the answer is NOW.
Over the next 5 to 10 years, you won’t be able to hire the right talent at the right cost. The most capable engineers are being absorbed by companies that are already investing in their data platforms today.
Companies that start early can iterate faster, build stronger foundations, and attract better talent over time. Think that the first hires will shape the trajectory of your entire data function.
As we’ve written in our guide on structuring your offshore development team, your first hires set the culture of your team. The engineers you hire now can become the natural leaders you need to step up as your offshore team scales. And if you’re going to bet on early hires shaping your team’s future, you want those people to be exceptional.
Real examples of companies that have built offshore data teams in Bangalore
The best way to show the value of offshore data engineers is to look at real examples of organisations that have built or extended their current teams in Bangalore, India, with great results.
How we helped Preqin build a 250+ people data team
Preqin is a leading provider of data, analytics, and insights for the global alternative assets industry, covering private equity, hedge funds, family offices, etc. When we started our collaboration with them in 2018, they were looking to increase their engineering capacity, which couldn’t scale quickly enough in London due to talent shortages and ballooning costs. As things progressed on the engineering front, they shifted their focus toward improving overall data quality.
Eventually, over 50% of the 450+ person GCC we built for them in Bangalore were data roles. The Bangalore team became Preqin’s largest globally. We moved them into a dedicated 5-story building in 2022 and transferred the operation to their own legal entity in India.
As Daniel (EVP of Global Operations) put it, they wouldn’t have grown as quickly without the talent hired in Bangalore.
We wanted to have our own team and make it very ‘Preqin’ and we wouldn’t have been able to build that team so quickly without The Scalers. They handled all of the hassles so we could spend our time building quality software and collecting quality data. Preqin wouldn’t have grown as much without the team in India.
Medionalum’s data team delivered invaluable insights
For asset management firms that typically handle large volumes of data, a strong data engineering capability is critical. Yet in many local markets, the shortage of skilled data talent and its high cost make building such teams quite challenging.
That was the case with Mediolanum, a Dublin-based Italian asset management firm, back in 2020. Their analytics team had the data but couldn’t leverage it properly. Legacy infrastructure was holding them back, and the European talent market for certified data analysts and engineers in asset management technologies was tight.
Drawing from our experience and network in Bangalore, we built them a 15-person data team to modernise data processes, build user-friendly dashboards, and help the analytics team deliver insights that directly supported investment decisions across the business. The engineers also played a key role in migrating Mediolanum’s infrastructure to the cloud.
The value The Scalers deliver is long term — Mediolanum Bangalore is an extended part of our team, not individuals disconnected in a different part of the world. It’s been fantastic and much smoother than I could have imagined.
Seven West Media’s small data team has produced big results
Seven West Media is Australia’s largest media company. When they came to us, they had a specific problem: they needed better Business Intelligence and actionable reporting across their broadcasting operations.
They’d previously worked with a vendor in the Philippines, but stability and quality were ongoing issues. The 4-person data team we built for Seven West Media in Bangalore has turned messy data into something leadership can act on.
This team is responsible for analysing viewer behaviour data, like how long audiences stay on a channel, how they engage with ads, and what content keeps them watching.
Building your offshore data team: The roles and structure you need
Now that we’ve shared success stories from companies that have built offshore data teams, it’s time to talk about how these teams are structured.
But first, let’s answer the burning question…
Do you really need an offshore data team?
If any of these sound familiar, the answer is likely yes:
- Your local data team isn’t able to deliver the value they should, and finding the right talent has become a persistent challenge.
- Your leadership team lacks access to structured, decision-ready data, which means critical decisions are made on instinct, fragmented inputs or outdated reports.
- Reporting is slow, manual, and low-value. Your analysts spend more time pulling data than analysing it.
- The reliability and quality of the data you’re capturing are questionable. Different systems say different things, and nobody knows which one to trust.
- Your data pipelines are inconsistent, which means even when you have the right data, it doesn’t flow into the tools and dashboards your teams depend on.
In other words, you do have the data, but that data isn’t ‘working’ for your business.
These challenges are rarely solved by incremental fixes or tooling alone. They require a dedicated data function with the right mix of engineering and analytics expertise.
An offshore team (built with the right model) can quickly and at scale bridge this capability gap.
How offshore data teams typically scale
You don’t need all five roles we listed above from the start. The pattern we’ve seen work best, and what we recommend to our partners, follows a logical progression.
Start with two to three data engineers who build your core pipelines and get data flowing reliably into a central warehouse. This is the unglamorous but critical work that everything else depends on.
Once the data is flowing and structured, you add an analytics engineer and a BI developer. The analytics engineer cleans and models the data, and the BI developer puts it into dashboards. Then, your leadership team will have access to the kind of reporting they’ve been asking for so long.
As your data operations grow and infrastructure complexity increases (more data sources, heavier query loads, stricter performance requirements), you bring in a data platform engineer to keep things optimised and stable.
And when the business is ready to move toward predictive analytics or deploy AI-driven features, that’s when you can consider hiring ML engineers.
The challenges you must be aware of when setting up your offshore data team
There are challenges that can slow you down or derail the whole effort if you’re not prepared for them. Here’s what we’ve seen go wrong most often.
Securing top-down sponsorship and business context
Many companies hire offshore data engineers and expect outstanding outputs from day one without having any business or industry context for their team. This approach rarely works.
Data engineering is beyond a technical function; it sits at the intersection of business logic, operational nuance and decision making. If leadership doesn’t invest time in explaining what matters, validating outputs, and giving ongoing feedback, even the best engineers will take months longer to deliver anything useful.
Yes, you should be spending money on this, but you should also spend time on training, explaining, and validating.
Just as important, you should move away from the idea that one data engineer can solve for everything. Role specification within data teams plays a key role, spanning pipeline and infrastructure development, data quality, and business enablement.
Having a single individual cover this entire spectrum can impact efficiency, whereas a well-balanced distribution of ownership can, in turn, align efforts with business goals.
Integrating with legacy systems and cloud infrastructure
Most systems operate across legacy systems and modern cloud technologies, so it’s quite challenging to bring data together without disrupting what already exists.
The real issue here is getting the clarity and control. Companies that handle this will focus less on simply connecting systems and more on creating a clear flow of data across the organisation.
You need data engineers who understand the criticality of data and route it to the right systems and stakeholders at the right time.
Managing data security, governance, and compliance
Once you start building data pipelines and connecting systems, you’re changing how data flows through your organisation, which has real implications for security, governance, and compliance.
Questions that never came up before suddenly become critical, such as who has access to what, how sensitive data is handled in transit, and what happens when a new data model changes the relationship between departments and their data.
These have direct implications for risk, compliance, and trust.
Any serious data operation, whether onshore or offshore, needs to have security and governance built into its design from day one. As these areas require deeper exploration, frameworks such as ISO 27001 for information security management, SOC 2 for data handling, and operational controls are useful benchmarks for data practices.
Narrowing it down, it’s very important to focus on data ownership and existing processes for monitoring and auditing data usage. If not, the cost of getting security and governance wrong can increase significantly.
Ensuring data quality and integrity
Organisations are investing more in data, but without strong data quality controls, this often leads to confusion. It introduces risks that are difficult to anticipate, particularly in business decision-making, and can lead to misalignment across teams that erodes trust in the system.
When we talk about the fundamentals of any data system for any company, the credibility of data sources is quite important; without it, even the most well-built pipelines and beautiful dashboards fail to deliver real value.
Driving adoption in the organisation
You can build the infrastructure, hire the team, and deliver perfectly modelled dashboards… and still fail if nobody in the organisation uses them. Adoption is the silent killer of data initiatives.
Think that reports need to reach the right people, in the right format. A CFO doesn’t want the same dashboard as a product manager. Making sure your data outputs are designed for their audience, and that people in the organisation are trained and encouraged to use them, is just as important as the engineering work behind them.
Hire offshore data engineers with a unique (and proven) model
If you’ve read this far, you’re likely ready to hire offshore data engineers. Maybe you want to build a team from scratch or just extend your current engineering capacity with a few specialists.
At The Scalers, we’ve built 130+ tech and data teams in the past decade in Bangalore, ‘The Silicon Valley of Asia’,for organisations who, just like you, weren’t able to find the right talent at home. Some of our partners have highlighted how we’ve hired engineers who are “perfect for our company culture.”
If you want to learn more about our offshore model and how we can help you set up your data engineering team in Bangalore, send us a message outlining your unique needs. One of our experts will get back to you shortly with a tailored solution.
FAQs
The same way you’d manage your local team. At The Scalers, the data teams we build work exclusively for our partners and integrate directly into their existing processes, tools, and methodologies.
Posting a job listing hoping to hire top data engineers won’t get you far, especially in a competitive market like Bangalore. What works best is having an offshore development partner with access to the local talent pool and a rigorous recruitment process. At The Scalers, every candidate goes through a 7-step recruitment process, which includes technical assessments, cultural fit evaluation, and multiple interview rounds, before they ever speak to you.
The biggest one: treat your offshore data team as part of your organisation, not as an external vendor. That means giving them full context on your business, your industry, and your data challenges. On the operational side, using the same tools, repositories, and communication channels as your local team reduces friction.
One of the hardest things to get right early on is matching your team composition to where you are in your data journey. If your problem is that leadership doesn’t have usable dashboards, you need a data engineer to build the pipeline, an analytics engineer to model the data, and a BI developer to visualise it. Hiring only a data engineer and expecting them to do all three will yield mediocre results. If in doubt, you can always rely on the expert point of view your offshore development partner can provide.
Hire the Right Data Engineers
With The Scalers’ offshore model, you can hire data engineers who work exclusively for you and operate inside your workflow from day one. Grow steadily, stay flexible, and work with people who care about the product as much as you do.
