About the Role You’ll work directly with the founder. Learning fast, owning small but meaningful pipeline tasks, and shipping production code exactly to spec. What You’ll Do In this role you’ll build and ship ETL/ELT pipelines in Python or Scala, crafting and tuning the necessary SQL transformations, while closely following my design documents and verbal briefs and iterating quickly on feedback until the output matches requirements. You’ll keep the codebase healthy by working through Git feature branches and pull requests, adding unit tests, and adhering to our pre-commit hooks. Day-to-day work will involve operating across AWS services such as EMR/Spark as projects demand. Learning is continuous: we’ll pair regularly for reviews and debugging, and you’ll present your progress during short weekly catch-ups. Must-Have Basics Up to 6 months practical experience (internship, project, or personal lab) in data engineering Working knowledge of Python or Scala and solid SQL Basic Git workflow familiarity Conceptual understanding of big-data tooling (Spark/Hadoop) Exposure to at least core AWS storage/compute services Strong willingness to take direction, ask questions, and iterate quickly Reside in Ahmedabad and commit to full-time office work Nice-to-Haves Docker or Airflow familiarity Data-modeling (star/snowflake, SCD) basics Hackathon or open-source contributions Compensation & Perks ₹15,000 – ₹30,000 / month (intern / junior band) Direct 1-on-1 mentorship from a senior data engineer & founder Dedicated learning budget after 90 days Comfortable workspace, high-end dev laptop, free coffee/snacks How to Apply Apply with your résumé (PDF). In the note, share a link to code or briefly describe a data project you built. Shortlisted candidates will have an on-site interview (python and SQL discussions) Location : S.G.Highway, Ahmedabad Timing : 8-9 hours (Flexible) Experience : 0 to 6 months If you’re hungry to learn, enjoy clear guidance, and want to grow into a full-stack data engineer, I’d love to hear from you. Show more Show less

About the Role You’ll work directly with the founder. Learning fast, owning small but meaningful pipeline tasks, and shipping production code exactly to spec. What You’ll Do  In this role you’ll build and ship ETL/ELT pipelines in Python or Scala, crafting and tuning the necessary SQL transformations, while closely following my design documents and verbal briefs and iterating quickly on feedback until the output matches requirements. You’ll keep the codebase healthy by working through Git feature branches and pull requests, adding unit tests, and adhering to our pre-commit hooks. Day-to-day work will involve operating across AWS services such as EMR/Spark as projects demand. Learning is continuous: we’ll pair regularly for reviews and debugging, and you’ll present your progress during short weekly catch-ups. Must-Have Basics Up to 6 months practical experience (internship, project, or personal lab) in data engineering Working knowledge of Python or Scala and solid SQL Basic Git workflow familiarity Conceptual understanding of big-data tooling (Spark/Hadoop) Exposure to at least core AWS storage/compute services Strong willingness to take direction, ask questions, and iterate quickly Reside in Ahmedabad and commit to full-time office work Nice-to-Haves Docker or Airflow familiarity Data-modeling (star/snowflake, SCD) basics Hackathon or open-source contributions Compensation & Perks ₹15,000 – ₹30,000 / month (intern / junior band) Direct 1-on-1 mentorship from a senior data engineer & founder Dedicated learning budget after 90 days Comfortable workspace, high-end dev laptop, free coffee/snacks How to Apply Apply with your résumé (PDF). In the note, share a link to code or briefly describe a data project you built. Shortlisted candidates will have an on-site interview (python and SQL discussions) Location : S.G.Highway, Ahmedabad Timing : 8-9 hours (Flexible) Experience : 0 to 6 months If you’re hungry to learn, enjoy clear guidance, and want to grow into a full-stack data engineer, I’d love to hear from you. Show more Show less