Data Engineer \u2013 Commercial Enablement

Job Description

Data Engineer u2013 Commercial Enablement

Mumbai | 2u20134 years experience | [Hybrid, X days a week in office / On-site]

We're the Commercial Enablement team at a large pharmaceutical company in India. We build and run the digital products our field force uses every day: sales performance tracking, incentive calculations, distributor and channel platforms, and the reporting that leadership looks at every week.

All of it runs on data, and we need people to build and look after that data layer properly.

What the job is

You'll build the pipelines that pull data from our sales, CRM, HR and external market sources, clean it up, model it, and make it available to dashboards and applications. Then you'll keep them running. This isn't a project role where you hand over and move on. You'll own what you build once it's in production.

You'll sit inside the product team, not in a central IT function. That means you'll hear directly from the people using the numbers, you'll be part of the discussion when requirements are set, and you'll know when something you built is being used in a leadership review.

What you'll own:

- Building and maintaining ingestion and transformation pipelines across multiple source systems

- Handling the messy stuff: late data, backdated corrections, restated months, duplicates

- Working on the commercial data model, including keeping history accurate when sales territories, managers, products or reporting lines change

- Putting data quality checks, monitoring and alerts in place so problems are caught before they reach a payout or a leadership review

- Troubleshooting when something breaks or two numbers don't match, finding the root cause, and fixing it properly

- Writing things down so the next person can understand them

What you'll get exposure to as you grow:

- Tuning queries and storage as we scale from a regional rollout to all of India

- Moving legacy data onto our cloud platform

- Reviewing what our vendors build and pushing back when it isn't good enough

- Working with our IT team on infrastructure, security and access

Owning pipelines in production means occasionally being the person who looks at a failure outside normal hours, usually around month-end or payout cycles. [Adjust or remove based on actual support arrangement.]

Who we're looking for

2 to 4 years working with data, including at least a year of hands-on work building or maintaining production pipelines that people depend on. You've worked with large datasets and you've had to make something faster or more reliable.

You don't need an engineering degree. Some of the best people we've come across in this space started in commerce, finance or economics and moved into data through certifications or simply by doing the work. If you've built real pipelines, we'd like to hear from you.

You may be a good fit if:

- You started on the business side (sales analytics, inventory, ERP) and moved into data engineering

- You're a data analyst or data scientist who ended up building the pipelines behind your own work and found you liked that part more

- You're a data engineer who wants to be closer to the business and the people using your work

Must have:

- Strong SQL. Comfortable with window functions and CTEs, and able to work out why a query is slow

- Python for transformation and validation work

- Experience building and running batch pipelines in production

- Hands-on experience with at least one major cloud data platform. We don't mind which one

- A working understanding of dimensional modelling: star schemas, grain, slowly changing dimensions

- Git, and some familiarity with testing and deploying changes to data pipelines

Good to have:

- Spark or another distributed processing framework

- Experience with an orchestration tool

- Enough experience with a BI tool to build datasets that reports can actually run on

- Streaming pipelines

- Experience in pharma, healthcare, BFSI or another regulated industry

- Some exposure to forecasting or predictive models, so you understand what downstream users need from your data

- Experience migrating legacy systems to the cloud

The right person is careful about getting numbers right, comfortable telling a stakeholder something isn't ready yet, able to explain a technical problem without jargon, and happy to take real ownership in a small team.

What we'd expect

In your first three months, get to know how data flows through our systems and take over at least one production pipeline. By six months, have quality checks and monitoring running on everything you own. By the end of your first year, you should be independently owning a meaningful part of the commercial data model and contributing to its rollout across India.

A lot of what this team produces ends up in people's incentive payouts and in leadership reviews. When the data is wrong, people notice. We need someone who takes that seriously.