We're hiring a Senior Hadoop Developer for our Saravanampatti office in Coimbatore. This is a full-time, on-site role paying ₹182,000 a month, sitting within our data engineering and BI group, and it's meant for someone who's already spent real time running production Hadoop workloads rather than just studying the ecosystem in a course. The team here handles both the underlying data infrastructure and the reporting layer built on top of it, so the role spans both ends of that pipeline.
The work itself
- Build and maintain data pipelines that move large volumes of raw data through HDFS into a structured, usable form
- Write and optimize MapReduce jobs and Hive queries against datasets that don't fit comfortably into a traditional relational database
- Turn that processed data into structured reports and dashboards that business stakeholders actually use to make decisions, not just static exports nobody opens again
- Maintain existing reporting workflows as source data and business requirements shift over time
- Tune slow queries, since a report that takes twenty minutes to load tends to get quietly abandoned by the people it was built for
The reporting side of this role connects directly back to the pipeline work. A dashboard is only as reliable as the data feeding it, so debugging a wrong number on a stakeholder's screen often means tracing it back through several transformation steps to find where it actually broke. That kind of root-cause tracing tends to fall to whoever built the pipeline in the first place, so ownership here runs from raw ingestion all the way through to the final chart someone in another department is looking at.
What we're asking for
A bachelor's degree is the baseline requirement, and most people applying at this seniority studied computing or engineering in some form, though we've seen strong candidates from adjacent backgrounds too. Alongside that, you'll need real experience working with large, messy, real-world datasets, not curated sample data from a training environment, plus solid SQL skills and hands-on time building data pipelines. The gap between working with tidy sample datasets and working with actual production data, full of duplicates, missing fields, and inconsistent formatting, is usually the first thing that separates a strong candidate from one who's only worked in a sandboxed environment.
We're looking for 5.5 years of experience in this space. That's enough time to have run into most of the usual Hadoop headaches: a job that runs fine on a small test cluster and then times out at production scale, or a Hive query that needs restructuring entirely once the underlying data volume triples. Candidates who've been through that kind of troubleshooting tend to ramp up noticeably faster here. We'd rather hear about one project where things actually went wrong and how you fixed it than a long list of tools someone's technically touched once.
Skills
Core requirements are Hadoop, HDFS, and MapReduce, plus Hive for querying, general SQL proficiency, and real end-to-end data pipeline design experience. These aren't nice-to-haves at this level; they're the daily toolkit.
Spark experience is a strong plus, since a growing share of our newer pipeline work is shifting that direction for performance reasons. Familiarity with ingestion tools like Sqoop or Kafka would help too, and some scripting ability in Python or Scala rounds things out well. On the reporting side, prior exposure to a BI tool such as Tableau or Power BI is useful, though it's secondary to the core data engineering skills. If you've built a dashboard that stakeholders actually kept using six months later rather than abandoning after the initial rollout, that's a better signal than which specific BI tool you happened to use.
Team and rhythm
Naukri Mitra has seen steady growth in data engineering listings out of Coimbatore over the past year, and this particular team supports reporting needs for several internal business units at once. Priorities shift depending on which stakeholder group has an urgent request, so a typical week might start with pipeline maintenance and end with rebuilding a dashboard a finance team needs reworked before month-end close. Deadlines tied to month-end and quarter-end reporting cycles tend to be the busiest stretches, and the team plans staffing around those periods rather than treating every week the same. Outside those windows, there's generally room to work on pipeline improvements that don't have an immediate stakeholder attached but pay off over time.
Compensation and benefits
This role includes the following in addition to monthly salary.
- Health insurance
- Paid time off
- Performance-linked bonuses
- Relocation assistance for candidates moving to Coimbatore
- Accommodation support during the initial transition
Given the seniority and pay level of this role, the relocation package is structured to actually cover a real move, rather than a token amount that barely dents moving costs. Performance bonuses here are tied to specific project outcomes rather than a blanket annual figure, which tends to matter more to candidates who've already reached this pay band elsewhere.
Location
The role is based on-site in Saravanampatti, Coimbatore, with no remote or hybrid option available. For experienced professionals looking at senior-level Hadoop developer jobs in Coimbatore, this role sits comfortably at the higher end of the local pay range. Saravanampatti has become one of the more established tech corridors in the city, so infrastructure and amenities around the office are generally well developed. For someone relocating from outside Tamil Nadu, the area has enough existing tech presence that finding housing nearby isn't the ordeal it might be in a less developed part of the city.
How to apply
Send in your application along with a short description of the largest dataset or pipeline you've personally built or maintained. We'll ask about scale specifically during the interview, since managing a few gigabytes and managing several terabytes call for genuinely different engineering habits. The process includes a technical discussion around a past production issue you've resolved, followed by a conversation with the reporting team you'd be working alongside.
