There's an Associate Spark Developer opening in Ranchi, based on-site in Lalpur, with a salary up to ₹87,500 per month. It's a full-time role in the Data Engineering & BI space, and it requires 15 months of hands-on experience with Spark, not just coursework or a bootcamp project. The team here works on production pipelines that already feed live business reports, so mistakes carry real consequences, not just a failed test suite.
One recurring headache this role exists to solve: a nightly batch job that used to finish around 2 AM started creeping past 5 AM as data volumes grew, and nobody noticed until a downstream report started showing up late for the morning stand-up. Fixing that kind of drift, through better partitioning, smarter joins, or just catching a job that's quietly become inefficient, is a normal part of the work rather than an occasional emergency.
- Design and maintain data pipelines that move information reliably from source to destination
- Pull together data from multiple source systems into something usable for analysis
- Build reports and dashboards that business teams actually rely on day-to-day
- Check data quality across systems so a bad upstream feed doesn't quietly corrupt a downstream report
Pipeline work here isn't a one-and-done exercise. A pipeline that ran fine at last quarter's data volume can quietly start to fall over as source systems grow, and part of the job is noticing that kind of gradual degradation before a business team does. Schema changes from an upstream team are another regular source of friction; a column that gets renamed or dropped without warning can silently break a join three steps downstream, and tracing that back to its source takes real familiarity with how the pipelines fit together.
What's needed to start
A bachelor's degree is the education requirement here, and computer science or a related technical field lines up naturally with the work, though what a candidate has actually built with Spark carries more weight in practice. Fifteen months of relevant experience is expected, whether that came from a single job, an internship followed by a short first role, or a mix of contract work. Someone who spent those fifteen months owning a real pipeline end-to-end, even a small one, will generally be a stronger fit than someone with the same duration spent mostly assisting on someone else's project.
The core skills for this one:
- Apache Spark, with a working understanding of how jobs actually get distributed across a cluster
- Scala or Python, whichever a candidate is stronger in, since both show up in the existing codebase
- SQL, comfortable enough to write and troubleshoot moderately complex queries
- A grasp of distributed computing concepts beyond just knowing the Spark API surface
None of the following are required, but they're genuinely useful: prior exposure to Databricks or a similar managed Spark environment, some familiarity with the broader Hadoop ecosystem including Hive, and any hands-on time with a workflow orchestration tool like Airflow. A candidate who's used Git comfortably enough to manage feature branches without help will also settle in faster, since most of the pipeline code here lives in a shared repository rather than scattered notebooks. These extras don't substitute for solid core Spark skills, but any one of them cuts down the ramp-up time noticeably.
Compensation
The role pays up to ₹87,500 per month, which is a solid figure for an associate-level Spark developer job in Ranchi, factoring in the 15 months of experience the role calls for. That figure sits ahead of what a lot of associate-level data roles pay locally, which reflects how specific and still relatively scarce solid Spark experience is compared to more general SQL or reporting skills.
- Health insurance
- Paid time off
- Provident fund contributions
- Relocation assistance for candidates moving to Ranchi
- Accommodation support during the initial move
Lalpur, Ranchi
The office is in Lalpur, a well-known, centrally located part of Ranchi with good access to most residential neighborhoods in the city. Naukri Mitra has been carrying a steady stream of Spark developer job openings in Ranchi over the past several listings, which points to real, ongoing demand for data engineering talent in the city rather than a one-off vacancy. It's an on-site role, so pipeline debugging and design discussions happen face-to-face with the rest of the data team rather than over a shared screen. Ranchi's data engineering scene is still fairly compact compared to Bengaluru or Hyderabad, which means fewer local competitors but also fewer peers to casually swap notes with outside the immediate team.
For someone early in a data engineering career, working on-site alongside more experienced engineers tends to shorten the path to actually understanding why a pipeline is architected a certain way, since the reasoning behind old decisions is easier to ask about in person than to reconstruct from commit history alone. A quick question over someone's shoulder about why a particular join was written a certain way often saves an hour of digging through old pull requests trying to piece the logic back together.
How to apply
Applications should include a resume and, where possible, a short note on a specific Spark project worked on, the data volume, and what made the job non-trivial. Generic statements like "worked with big data" tell a reviewer very little compared to a concrete detail about a real pipeline. Shortlisted candidates go through a technical round covering Spark fundamentals and a SQL exercise, followed by a conversation about the kind of pipelines this team maintains day to day. Most candidates hear back within a week or two of the final round, and there's no separate written test beyond the technical round itself.
Anyone unsure whether their fifteen months of experience "counts" for this role, because it was split across a short contract and a longer internship, for instance, should apply anyway rather than self-select out. The hiring team looks more closely at what was actually built than at how the timeline is packaged on paper.
