- Data engineers build the infrastructure that makes data usable (pipelines, warehouses, ETL systems), while data scientists use that infrastructure to extract insights, build models, and inform business decisions.
- Most companies going offshore need data engineers before data scientists. Without clean pipelines and a reliable data warehouse, a data scientist has nothing to work with, which is why the engineer usually comes first.
- Although there’s always a custom element to building data teams, the typical scaling structure is: start with 2–3 data engineers to build your core pipelines, then layer in analytics engineers, BI developers, and platform engineers. ML engineers come last (if at all).
- India (and Bangalore in particular) offers a deep talent pool of data engineers. Many backend and full-stack engineers are moving into data roles, and the country’s mature data and AI ecosystem provides the opportunities they need to grow.
If you search “data scientist vs data engineer,” you’ll find dozens of articles explaining the difference between the two roles. Most of them are written for job seekers trying to pick a career path.
This article isn’t one of those.
We’re writing this for CTOs and tech leaders who need to make a hiring decision: which role should you hire, what does each one do for your business, and which one brings more value to your offshore team?
Let’s break it all down.
Data scientist vs data engineer: What’s the actual difference?
A data engineer focuses on making the data usable at scale. Their work takes place behind the scenes, maintaining the infrastructure that enables data to flow, integrating systems, ensuring data accuracy, and building pipelines. They are the reason dashboards refresh on time and analytics teams function efficiently.
A data scientist extracts intelligence from the data. They work on building predictive models, running statistical analyses, and helping leadership with smarter decision-making.
Skills comparison: Data scientist vs data engineer
Both roles require strong technical foundations, but their skill sets differ. Here’s a side-by-side comparison:
| Data engineer | Data scientist | |
| Core focus | Data infrastructure and pipelines | Analysis, modelling, and business insights |
| Programming languages | Python, Java, Scala, SQL | Python, R, SQL |
| Key tools | Apache Spark, Airflow, Kafka, dbt, Fivetran, Flink, Talend | TensorFlow, PyTorch, scikit-learn, Jupyter |
| Warehousing & platforms | Snowflake, Redshift, BigQuery, Databricks | Primarily querying, not designing |
| Cloud platforms | AWS, GCP, Azure | AWS SageMaker, GCP Vertex AI, Azure ML |
| Databases | PostgreSQL, MongoDB, Cassandra | Primarily querying, not designing |
| Visualisation | Tableau, Looker Studio, Power BI, Omni | Matplotlib, Seaborn, Plotly |
| Software engineering | Strong. Production-grade code, CI/CD, Docker | Moderate. Experimental code, notebooks |
| Business interaction | Works with engineering and DevOps teams | Works with leadership and product teams |
| Education background | Computer science, software engineering | Statistics, mathematics, physics, computer science |
Day-to-day responsibilities: What each role does
A data engineer’s day looks something like this:
- Pipeline construction and maintenance: Building the systems that move data from source systems (APIs, databases, SaaS tools) into a centralised warehouse.
- Monitoring and incident response: Watching pipeline health, debugging failures when data stops flowing, and preventing downstream disruptions.
- Performance optimisation: Tuning queries and schemas so that dashboards and reports load in seconds, not minutes.
- Data modelling: Designing database structures that support reporting, analytics, and long-term scalability.
- Cross-functional collaboration: Working with data scientists and analysts to make sure they have access to clean, well-structured data.
- Infrastructure management: Scaling systems as data volumes grow and keeping cloud costs under control.
A data scientist’s day looks something like this:
- Data analysis: Diving into datasets to surface patterns, anomalies, and opportunities the business hasn’t spotted yet.
- Model building: Training machine learning models for predictions, recommendations, classification, or segmentation.
- Experimentation: Designing and analysing A/B tests to measure the impact of product or strategy changes.
- Data visualisation: Building dashboards that translate complex findings into language that leadership can act on.
- Stakeholder presentations: Presenting results to executives and translating statistical outputs into business decisions.
- Data requests: Collaborating with data engineers to request new data sources or pipeline modifications.
Data scientist vs data engineer: Salary & cost comparison (onshore vs offshore)
One of the first questions tech leaders ask when comparing these roles is cost. Here’s what you can expect to pay for each role across key markets:
Data engineer salary comparison (annual, USD)
| Seniority | US | Europe | India (offshore) |
| Junior | $95,000–$125,000 | $50,000–$80,000 | $15,000–$30,000 |
| Mid-level | $130,000–$175,000 | $80,000–$120,000 | $40,000–$50,000 |
| Senior | $175,000–$230,000 | $110,000–$155,000 | $60,000–$80,000 |
Data scientist salary comparison (annual, USD)
| Seniority | US | Europe | India (offshore) |
| Junior | $90,000–$120,000 | $45,000–$75,000 | $12,000–$25,000 |
| Mid-level | $120,000–$165,000 | $75,000–$115,000 | $35,000–$45,000 |
| Senior | $165,000–$220,000 | $105,000–$145,000 | $60,000–$70,000 |
Sources: Glassdoor, Levels.fyi, PayScale, LinkedIn Salary Insights (2026).
A note on pricing: These salary ranges are useful for benchmarking, but they reflect base compensation in different markets and can vary based on factors such as engineers’ locations, the specific tech stack required, and the hiring and engagement model used to hire talent.
Should you hire a data scientist or a data engineer first?
There’s no universal answer here because every company’s data maturity is different.
We’ve noticed many businesses rush into bringing in data scientists, attracted by the promises of AI, predictive analytics, and smarter decision-making. But as Adith Khan, Director of Partner Success at The Scalers, puts it: “AI models mean little if your data is trapped in silos and the pipelines keep breaking.”
This is exactly where data engineers are needed to build scalable systems, laying the foundation for everything else to run on.
The value of your data increases drastically once the foundation is in place. Clean, accessible and reliable data allows teams to move faster, builds trust in your reporting and decision-making and creates the right environment for analytics, automation and machine learning initiatives to succeed.
This doesn’t outweigh the importance of having a data scientist. If your infrastructure is already mature and your data is organised, a data scientist can uncover patterns, forecast trends, and identify growth opportunities. In this scenario, your business is ready to move from just managing data to maximising it.
When building an offshore development team, the optimal hiring strategy depends on the team’s core challenges:
- Hire a data engineer to solve data accessibility, scalability, and reliability.
- Hire a data scientist for initiatives requiring strategising, innovation, and optimisation.
Companies that go offshore rarely stop at one role. They build in stages, first hiring talent to solve current challenges, then expanding capabilities to support the business.
Why are data engineers so valuable for offshore teams?
We’ve written an in-depth guide about why data engineers have become the most valuable offshore hire, but here’s a quick summary:
- They sit at the core of everything. Data engineers build the systems that power AI, analytics, automation, and business intelligence. Without them, none of these functions works.
- Their impact is business-critical. Data engineering connects directly to decision-making. The pipelines they build determine whether leadership gets reliable, real-time insights or outdated, fragmented reports.
- They enable every other data hire. Once a data engineer sets up the infrastructure, you can bring in analytics engineers, BI developers, and eventually data scientists. The engineer is the foundation that makes every subsequent hire productive.
For companies building offshore teams, data engineers offer the best starting point because they deliver immediate, tangible value while laying the groundwork for everything that comes after.
Why India is an ideal location for hiring data engineers & data scientists
If you’re going offshore for data talent, India, and Bangalore specifically, should be at the top of your list. Here’s why:
- The deepest data talent pool in the world. India has the largest concentration of data engineering professionals among the most popular offshore destinations… and that talent pool is growing. Backend and full-stack engineers in cities like Bangalore are moving into data roles because they offer greater leverage and better career growth. This means the supply of skilled data engineers is expanding at a time when demand elsewhere is outpacing supply (data engineering is one of the top fields with the most critical skills gaps among US and European companies).
- A young, motivated workforce. The country produces over 1.5 million engineering graduates annually, ensuring a continuous pipeline of fresh talent entering the data field.
- Engineers with experience working with Western companies. Thanks to the 550+ Global Capability Centres (GCCs) operating in Bangalore, many data engineers and scientists already have experience working with US, European, and Australian organisations.
- Cost effectiveness (cut costs by 25-40% without sacrificing quality). Hiring data engineers and data scientists in India costs a fraction of what you’d pay in the US or Europe.
- A mature ecosystem for data and AI. Bangalore has the highest adoption of modern data stacks in India and ranks among the top AI talent hubs globally. Engineers here work with the same tools and platforms as their counterparts in Silicon Valley.
For a deeper look at why Bangalore stands out among global tech hubs, read our guide on what makes Bangalore the ideal IT offshoring destination.
How The Scalers can help you build your offshore data team
At The Scalers, we’ve built 130+ tech and data teams in Bangalore in the past decade for organisations that couldn’t find the right data talent at home.
Preqin, a leading provider of data and analytics for the alternative assets industry, is a good example. They came to us in 2018 needing to scale their engineering capacity beyond what London could offer. Over time, their focus shifted toward data quality, and eventually, over half of the 450+ person R&D Centre we built for them in Bangalore were data roles.
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. And we wouldn’t have grown as Preqin without the team in India.
Here’s how we work:
- We find the best data talent. Every candidate goes through a rigorous 7-step recruitment process. You get the final say on who joins your team.
- We set up your operations. Private, branded office space, HR, payroll, compliance, and security, all handled on the ground.
- We keep your engineers committed. Ongoing engagement and support so your team stays motivated and stays long-term.
Ready to build your offshore data team? Send us a message outlining your needs. One of our experts will get back to you with a tailored solution.
FAQs
A data engineer focuses on building the systems that collect, store, and move data across your organisation (pipelines, warehouses, and ETL processes). A data scientist works with that data to find patterns, build predictive models, and generate insights that inform business strategy. The engineer builds the infrastructure; the scientist extracts value from it.
Not really. The skill sets are quite different: data engineering is rooted in software engineering and infrastructure, while data science requires deep knowledge of statistics and machine learning.
It depends on seniority, tech stack, and the hiring and engagement model you use to hire them. As a reference, a mid-level data engineer in India typically costs between $40,000 and $50,000 annually, and a mid-level data scientist ranges from $35,000 to $45,000.
Look for strong proficiency in Python and SQL, experience with cloud platforms (AWS, GCP, or Azure), and hands-on expertise with tools like Apache Spark, Airflow, dbt, and Snowflake. Beyond the tech stack, look for engineers who understand data modelling, can design scalable pipeline architectures, and have experience working in distributed teams. Communication skills and the ability to work autonomously are just as important as technical depth.
Data engineers build and maintain the data infrastructure, and data scientists consume that infrastructure to run analyses, build ML models, and generate insights. In practice, data scientists often request new data sources or transformations, and data engineers implement them. The relationship works best when both roles collaborate closely, using shared tools and communicating clearly about data requirements and priorities.
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.
