About the role
You'll get broad exposure across the full data flow—not boxed into one narrow task. You'll wear
multiple hats: this is primarily a data engineering role, but you'll also work hands-on with QA
and testing.
We're looking for someone early in their career with genuinely strong Python and SQL who
builds things that don't break quietly. When something does break, you dig in and find out why.
We care less about tools you can list and more about whether you understand why you do what
you do—and whether you think about what happens when the data is wrong.
What you'll do
What we're looking for
Write and optimise SQL that's correct, readable, and fast. Diagnose queries that aren't.
Clean and transform real-world data in Python/Pandas—handling missing values, bad
formats, and duplicates that actually show up, not just tidy demo datasets.
Extract data from messy and inconsistent sources (manual files, documents, systems
without clean APIs) and turn it into structured, usable data.
Build and maintain pipelines that pull data from multiple sources (databases, CSV/Excel,
APIs) into clean, analytics-ready tables.
Test and validate your own work—write checks, catch bad data early, and build validation so
failures surface loudly instead of producing silent errors.
Debug when pipelines fail or numbers look off—trace problems to their source rather than
patch symptoms.
Help with QA across the data you produce—define what "correct" means, spot
inconsistencies, and keep data quality high as the company grows.
Design sensible data models—knowing when to normalise and when a flatter, report friendly shape is right.
Work directly with analysts and stakeholders to understand what they need, then deliver
reliable data.
0-2 years of hands-on data experience (internships, projects, coursework). Show us
something you've actually built.
Strong SQL: joins (including non-obvious ones), aggregation, window functions, CTEs—and
know when to use them, not just recite them.
Strong Python, especially Pandas for cleaning, transforming, and extracting data.
A debugging instinct—when something's wrong, you're curious about why and can work
backward to root cause.
A testing and data-quality mindset—you assume input will be messy, check your output, and
catch problems yourself.
Good judgement about trade-offs—you pick simple solutions for simple problems and know
the fanciest approach usually isn't right.
Clear communication—you can explain technical choices to non-technical people and be
honest about what you've done versus read about.
Comfort with ambiguity and willingness to pick up whatever the work needs in a startup
environment
Pay: ₹25,000.00 - ₹50,000.00 per month
Benefits:
- Paid time off
- Provident Fund
Work Location: In person
