Data Engineering Intern at Fam — Bengaluru, Karnataka, India | LinkedIn Jobs

About Fam (previously FamPay)Fam is India’s first payments app for everyone above 11. FamApp helps make online and offline payments through UPI and FamCard. We are on a mission to raise a new, financially aware generation, and drive 250 million+ young users in India to kickstart their financial journey super early in their life.We’re reimagining how the next generation experiences fintech—going beyond payments to build a lifestyle brand that blends money, identity, and everyday experiences into one seamless, intuitive journey.Founded in 2019 by IIT Roorkee alumni, Fam is backed by some of the most respected investors around the world like Elevation Capital, Y-Combinator, Peak XV (Sequoia Capital) India, Venture Highway, Global Founder’s Capital and the likes of Kunal Shah, Amrish Rao as angel investors.About the roleWe are looking for Data Engineering Interns to work closely with the Fam Data Team in building and operating our data platform. You will contribute to our lakehouse, data pipelines and data quality checks, which power analytics, product and compliance reporting for a UPI/fintech platform serving millions of users. You will begin with well-scoped tasks under the guidance of a mentor and progressively take end-to-end ownership of individual pipeline tasks.On the JobPlatform Understanding: Learn how our OLTP source systems feed the OLAP lakehouse. Read table schemas, including column types and partition columns, and trace the end-to-end flow of an individual Airflow DAG taskSQL & Transforms: Write and modify SQL queries and simple Spark/SQL transformations under guidance, and validate the correctness of their outputPipeline Quality: Add basic row-count and null checks, along with meaningful logging, to the pipeline tasks you own. Understand the importance of idempotent pipelines and apply this principle in your changesPipeline On-call (Shadow): Participate in the pipeline on-call rotation alongside an experienced engineer. Identify failed runs and data freshness breaches, escalate with the relevant DAG and run details, and execute existing backfill playbooks under guidanceCost-aware Querying: Apply partition filters instead of full table scans, and understand that storage grows with data volume and every query incurs compute costAnalyst Support: Collaborate with product analysts as a peer by fixing queries, directing them to the right datasets in the data catalog, and publishing simple, reusable viewsAI-assisted Data Work: Use the tools provided, such as the Trino MCP, Text-to-SQL and data discovery tools, in day-to-day work, while ensuring that PII is never included in promptsAI-assisted Development: Use AI coding assistants (Cursor, Claude Code, Copilot or similar) to accelerate development tasks such as writing SQL, transformations, tests and scripts. Review, test and fully understand all generated code before raising a pull request; accountability for the code remains with youExperimentation: Identify small improvement ideas, build prototypes and present them during sprint reviews. The focus is on learning rather than measurable impactMust-haves:Final-year student or recent graduate in Computer Science, Information Technology or a related field, or equivalent practical experienceClear understanding of OLTP vs OLAP systems and their respective use casesAbility to write correct, readable SQL, including joins, aggregations, filters and basic window functionsWorking knowledge of Python; familiarity with Scala or Java is a plusAbility to read a table schema and explain the data it representsBasic understanding of how LLM / GPT models work, including tokens, context windows, prompting and the causes of hallucinationPractical experience using AI coding assistants for development tasks, with the judgement to verify their outputAt least one academic project or internship involving data or backend engineering that you can explain in detailProficiency with Git, Linux and the command lineA curious mindset: you question unexpected data patterns and escalate issues earlyGood to haveExposure to Apache Spark, Airflow or any data processing or orchestration frameworkUnderstanding of idempotency, partitioning and columnar file formats such as ParquetAbility to read a simple query plan and identify full table scansExperience running an open-weight LLM locally (Ollama, llama.cpp or similar) or building a small embedding and similarity search prototypeBasic understanding of Retrieval-Augmented Generation (RAG), including chunking, embeddings, retrieval and grounding responses in source dataExposure to vector databases such as pgvector, Qdrant, Chroma or OpenSearchFamiliarity with AWS fundamentals such as S3 and IAMExposure to BI tools such as Superset, Metabase or Power BIInterest in fintech, payments or data privacyWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Show more Show less Seniority level Not Applicable Employment type Volunteer Job function Information Technology Industries Financial Services