We build dedicated data engineering teams in Bangalore, India, that work as an extension of your company. Your team, your workflow, your processes.
Since 2014, we’ve set up 130+ offshore tech and data teams for global organisations like yours, from a few engineers to innovation hubs with hundreds of professionals.
We source the best data engineers, but you have the final say on who joins your team. They work exclusively for you. No shared resources, no divided attention.
Access Bangalore’s 2M+ talent pool. Find senior AI-capable data engineers with in-demand skills that are hard to find at home, including specialists with experience in your industry.
Your data engineers absorb your business context fast, integrate with your workflows, and deliver outcomes that leadership can act on.
Unlike outsourcing, where data engineers are just temporary resources you add to your team, at The Scalers, we build teams with offshore data engineers who are fully committed to your business.
Your data engineers work only for you, full-time. They sit in your repositories, in your tools, in your stand-ups, and report into your management structure.
Resources are often shared across clients, with the same engineer working on several projects. The agency might swap them whenever workload demands shift.
Full integration with your tools, processes, and culture. Your data engineers absorb your company’s DNA from day one. You get an in-house feeling.
The agency manages the relationship; you’re kept at arm’s length. Engineers usually report to an agency project manager rather than to you directly.
11% attrition rate (vs a market average of 30-35%). Engineers stay long-term because we invest in retention through engagement programmes.
High churn is the norm. Agencies rotate people, and engineers leave for higher-paying roles. Every new engineer brings 4-8 weeks of ramp time and zero context.
A single all-inclusive monthly fee that includes everything, from salary and health insurance to HR, recruitment, administration, and account management.
Often quoted per project or per hour, with an opaque margin layered on top of the engineer’s cost. Hidden fees may surface as the work progresses.
Your data engineers work only for you, full-time. They sit in your repositories, in your tools, in your stand-ups, and report into your management structure.
Resources are often shared across clients, with the same engineer working on several projects. The agency might swap them whenever workload demands shift.
Full integration with your tools, processes, and culture. Your data engineers absorb your company’s DNA from day one. You get an in-house feeling.
The agency manages the relationship; you’re kept at arm’s length. Engineers usually report to an agency project manager rather than to you directly.
11% attrition rate (vs a market average of 30-35%). Engineers stay long-term because we invest in retention through engagement programmes.
High churn is the norm. Agencies rotate people, and engineers leave for higher-paying roles. Every new engineer brings 4-8 weeks of ramp time and zero context.
A single all-inclusive monthly fee that includes everything, from salary and health insurance to HR, recruitment, administration, and account management.
Often quoted per project or per hour, with an opaque margin layered on top of the engineer’s cost. Hidden fees may surface as the work progresses.
There’s no universal answer to when a company should hire its first data engineer. It depends on several factors, like the size and maturity of your data function.
The seven scenarios below are some of the most common triggers, the points where the decision usually becomes obvious to CTOs and business leaders.
Moving from on-premises systems or fragmented reporting environments to platforms like Snowflake, BigQuery, or Redshift becomes significantly more complex when multiple integrations, reporting dependencies, and historical datasets are involved.
At that stage, companies usually need dedicated ownership of the migration and the long-term data platform.
As businesses grow, data becomes spread across SaaS platforms, internal applications, transactional databases, APIs, and event streams.
Without a dedicated data engineering function, reporting logic often becomes fragmented across teams, leading to inconsistent metrics, duplicated transformations, and unreliable reporting.
AI and machine learning systems depend on reliable, structured, high-quality data.
Before models can be trained effectively, organisations need governed datasets, scalable pipelines, and consistent transformations. Otherwise, data scientists spend most of their time cleaning and restructuring data instead of building models.
Real-time systems require a different architecture from traditional batch analytics.
Applications such as fraud detection, personalisation, operational monitoring, dynamic pricing, and live analytics depend on streaming infrastructure and event-driven processing systems.
As more teams rely on analytics, reporting inconsistencies become more visible.
Different dashboards start reporting different numbers for the same metrics, business definitions stop aligning across departments, and teams create duplicate logic across reporting layers.
Many organisations still rely on legacy ETL infrastructure built around older tools, custom scripts, or undocumented workflows.
Modernising these systems requires an understanding of both legacy architectures and modern data tooling such as dbt, Airflow, and managed ingestion platforms.
As data operations grow, governance and compliance become engineering responsibilities.
Organisations operating under the GDPR, internal audit requirements, or sector-specific regulations need clear controls over data access, data lineage, retention policies, and sensitive data handling.
Offshore data engineers build the infrastructure that makes your data usable. They design and maintain the systems that collect, store, and process information at scale, so your analysts and data scientists can actually do their jobs.
Build and maintain the workflows that move data from source systems (APIs, product databases, SaaS tools, event streams) into your warehouse or lake. Decide whether each pipeline runs batch or streaming, schedule the jobs, and monitor execution end-to-end.
Design schemas, optimise queries, and maintain data quality across both transactional and analytical stores. Tune query performance, manage indexes and partitions, and document the data model so analysts and engineers can rely on it in the long term.
Build and manage your data infrastructure on AWS, GCP, or Azure, including warehouses, compute, storage, and access controls. Set up secure, scalable environments, monitor performance, and keep cloud costs predictable as your data volume grows.
Work with large-scale datasets using distributed computing frameworks like Apache Spark and Flink, typically run on cloud object storage (S3, GCS, Azure Blob). Process terabytes or petabytes efficiently without overprovisioning your infrastructure, and design jobs that scale with your data.
Structure your data for speed, accuracy, and long-term maintainability. Design dimensional models, star schemas, or whichever architecture fits your analytics requirements, document the model end-to-end, and keep business logic consistent across teams.
Orchestrate complex data operations using tools such as Airflow, Dagster, or Prefect. Schedule jobs, manage dependencies between pipelines, monitor execution, and build alerting and observability to keep your data layer reliable as it scales.
Build the data foundations AI and ML projects need. Set up feature stores, manage training-data pipelines, integrate with MLOps platforms such as SageMaker, Vertex AI, or Azure ML, and ensure your model-serving layer gets fresh, reliable data in production.
If you decide to hire data engineers through offshoring, you’ll enjoy a handful of benefits you wouldn’t be able to leverage by building a team locally or via other hiring models such as outsourcing or nearshoring.
Cut costs by 30-40% without sacrificing quality. For the price of 3 data engineers in the UK (~£160k), you can hire 7 in Bangalore with the same capabilities.
Hire data engineers with the tech stack you need: PostgreSQL, MongoDB, AWS Redshift, BigQuery, Snowflake, Databricks, Apache Spark, Kafka, Airflow, and more.
Scale your team as your business grows. Add data engineers when priorities shift, when you’re building a new product, or when you need to move faster.
Time zone differences can be an operational advantage. When your US or European team logs off, your Bangalore team picks up, implements, tests, and pushes updates.
Maybe you’re planning to extend your team with offshore data engineers or set up an offshore development centre. Either way, you need a structured plan to do it successfully. The three steps below will help you find the right talent, especially if it’s your first time offshoring.
You need clarity on what problem you’re trying to solve with data engineering. “We need data engineers” is not a requirement in itself.
Start by defining the scope of the work:
Then define the hiring shape:
The clearer your requirements are at this stage, the easier it is for an offshore development partner to match you with engineers who can operate in your environment instead of generic “data profiles”.
However, if in doubt, your partner will assist you in preparing a tailored proposal to meet your unique requirements.
Once your requirements are clear, the next step is choosing a reliable offshore development partner that can help you source and hire data engineers. Here’s what to look for:
This is where both sides confirm expectations in detail so the partnership can run smoothly from day one. The cleanest engagements set expectations upfront so neither side is surprised three months in.
Key areas typically covered before signing the contract include:
Once everything is clear and the contract is signed, the partner moves into execution mode: sourcing and shortlisting engineers, conducting interviews, and introducing you to candidates who suit your needs.
Within approximately 45–60 days, your offshore team is fully onboarded and operational, integrated into your delivery workflows, working within your systems, and reporting directly into your organisation.
Bangalore is known as ‘The Silicon Valley of Asia’ and has become an ideal IT offshoring destination for companies seeking the right data engineers.
Bangalore has the largest concentration of tech talent in India. Over 2 million software developers live and work here, with 90,000 new engineering graduates joining every year.
Over 40% of India’s 1,750+ GCCs are in Bangalore, employing 800,000+ professionals. Tech giants like Amazon, Google, and Microsoft run major operations here.
The data engineering talent pool runs deep. You can find specialists with experience in your tech stack and industry.
2,363 startups, 32 active unicorns, ranked #10 globally. Engineers here have built large-scale data systems for Flipkart, Ola, and Swiggy, managing billions of daily transactions.
Indian time is GMT+5:30 with no daylight saving time variations. This provides at least six hours of daily overlap with most global locations.
Hiring data engineers in Bangalore costs significantly less than the same role in the US or Western Europe.
Here are some examples of data engineering teams we’ve built in Bangalore for industry leaders.
You choose the engineers and how you want to build your team. We handle everything else: recruitment, onboarding, HR, payroll, office administration, engagement, and more. Here’s how it works.
You choose the engineers and how you want to build your team. We handle everything else: recruitment, onboarding, HR, payroll, office administration, engagement, and more. Here’s how it works.
Whatever role or technology you’re looking for, we hire only the best with our rigorous 7-step recruitment process.
Your new team will have a dedicated workspace, and we’ll handle everything for you on the ground: HR, payroll, compliance, and security, so you can focus on your work.
We make sure your engineers feel valued, supported, and genuinely connected to your company, which results in higher engagement, stronger performance, and long-term retention.
Read our latest articles and guides on data engineering, plus our leadership’s perspective on where the industry (and offshoring) are headed.
The questions CTOs and other business leaders ask us most often when hiring data engineers.
Data engineers build and maintain the infrastructure that makes data usable, including pipelines, databases, warehouses, and ETL processes. Data scientists create predictive models and run experiments. Data analysts interpret data and create reports for business decisions.
When they’re migrating to a cloud data warehouse, building real-time pipelines, consolidating data from multiple sources, preparing infrastructure for AI/ML, scaling analytics, replacing legacy ETL systems, or improving data quality and governance.
It depends on seniority and tech stack, but hiring offshore typically saves 30-40% compared to local hires (Europe) and 50% in the US. The key is to compare fully loaded costs, not just salaries. For example, a senior data engineer in the US earning $130,000 costs $180,000- $195,000 after benefits, payroll taxes, office space, equipment, and HR. Our prices include all of that.
Location is the starting point, but it’s rarely the only one. The stack and specialisation matter just as much: data engineers who work with specific tools like Spark, dbt, or Snowflake, or who have deep experience in a niche like financial data or healthcare pipelines, will cost more than a generalist profile. Seniority is another obvious one: a principal engineer with a decade of experience commands a different rate than someone at the 3–5 year mark. Then there’s the operating model: some providers advertise attractive day rates but layer on recruitment fees, office costs, and admin charges that only show up later. And finally, the quality of your offshore partner, a firm with a proven track record and low attrition, may charge more upfront, but you get better output, faster onboarding, and engineers who stay long term.
Some of the most common mistakes when hiring offshore data engineers include focusing solely on cost-cutting rather than finding engineers who are the right fit for your company, and overlooking whether the engineer has experience in your specific industry.
India, Poland, and Colombia are the three most established offshore destinations for data engineering talent. India has the deepest talent pool, with Bangalore as the standout hub thanks to its mature data and AI ecosystem.
Treat your offshore data engineers exactly like your in-house team. Same tools (Git, CI/CD, project management, Slack), same processes (sprint planning, standups, code review), same reporting structure. From day one, your engineers join your standups, follow your sprint cadence, and absorb your business context, and your Partner Success Manager handles everything operational (HR, payroll, office, retention) so you don’t have to. We also recommend at least one face-to-face exchange within the first 6 months: either someone from your team visits the Bangalore office, or your Indian team leads visit your HQ.
Yes. With The Scalers, every data engineer we hire is dedicated full-time to your team. They work in your tools, follow your processes, attend your standups, and report into your management structure. No shared resources, no rotating staff, no engineer juggling two clients in parallel.
You do. All code, pipelines, models, and documentation produced by your data engineering team are your property. We sign NDAs and IP assignment agreements as part of the standard contract, and your engineers commit directly to your repositories and your infrastructure. There’s no ambiguity about ownership.
At The Scalers, we use a 7-step recruitment process to find the best data engineers for your team:
We are market and vertical-agnostic. We have experience working across many sectors, including regulated sectors such as finance.
We’re ISO 27001 certified by TÜV SÜD, with information security built into how we operate. Every employee passes a background check and signs a comprehensive NDA before joining, and our Bangalore office runs 24/7 security guards, CCTV coverage, and card-based access control across all common areas. On the digital side, your data engineers work inside your security perimeter (your VPN, your SSO, your access controls, your audit logs), and our IT team can extend or replicate any additional measures you need, disabled USB ports, custom software provisioning, dedicated connections, tokenised VPNs, 802.1X-authenticated WiFi. We’ve never had a data breach in 10+ years of operation, and we continuously review our systems and processes.
We monitor performance and engagement closely as part of our retention work, but if a data engineer isn’t the right fit, we initiate a PIP (Performance Improvement Process). If that doesn’t work out, we kick off a new process to recruit new engineers.
No, we don’t provide software or apps, as your data engineers will use the same tools you use back home. However, we provide all the equipment your teams need to work, including laptops and monitors.
Fill in your details, your data is 100% confidential and secure
One of our team members will contact you promptly for a brief discovery call
We’ll prepare a solution matched to your needs and timeline
The controller of your data is The Scalers Ltd. with its registered office at Adelaide Road 13, D02 P950 Dublin, Ireland. The legal basis of the processing is our legitimate interest in responding to your questions and requests submitted through the form. You have the right to object to the processing, right to access the data, right to rectify or erase the data and also the right to restrict the data processing, in accordance with the Privacy Policy.
