Data Engineering Intern

InternHunt; Bengaluru; On-site; INR 167,000 - 391,000; Part time

Job summary

Fam is India’s first payments app for everyone above 11. FamApp enables online and offline payments using UPI and FamCard. We are building a data-driven platform with a lakehouse architecture and a strong focus on data quality.

This internship offers mentorship, hands-on tasks, and real ownership of pipeline components. You will work closely with the Fam Data Team to learn, implement SQL transformations, and contribute to analytics, product, and compliance reporting for a fintech platform

Qualifications

• Final-year student or recent graduate in Computer Science or Information Technology
• OLTP vs OLAP systems and their respective use cases
• Ability to write correct, readable SQL including joins, aggregations, filters and basic window functions
• Working knowledge of Python; familiarity with Scala or Java is a plus
• Ability to read a table schema and explain the data it represents
• Basic understanding of how LLM / GPT models work, including tokens and context windows
• Practical experience using AI coding assistants for development tasks, with the judgement to verify their output
• At least one academic project or internship involving data or backend engineering
• Proficiency with Git, Linux and the command line
• A curious mindset: you question unexpected data patterns and elevate issues early

Responsibilities

• Build and maintain data pipelines powering analytics and reporting
• Collaborate with data team to improve data quality and lakehouse performance
• Participate in on-call rotation and backfill playbooks under guidance
• Learn end-to-end data flow from OLTP source systems to OLAP lakehouse
• Apply partition filters to reduce cost and improve performance
• Help product analysts by fixing queries and publishing simple views
• Use AI tools to accelerate SQL/script development with manual review

Skills

SQL

Python

Apache Spark

Airflow

Git

Linux

Education

Bachelor's degree in Computer Science / IT

Tools

Apache Spark

Apache Airflow

Trino MCP

Superset

Metabase

Power BI

Job description

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 role

We 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 Job

Platform 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 task

SQL & Transforms:Write and modify SQL queries and simple Spark/SQL transformations under guidance, and validate the correctness of their output

Pipeline 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 changes

Pipeline On-call (Shadow):Participate in the pipeline on-call rotation alongside an experienced engineer. Identify failed runs and data freshness breaches, elevate with the relevant DAG and run details, and execute existing backfill playbooks under guidance

Cost-aware Querying:Apply partition filters instead of full table scans, and understand that storage grows with data volume and every query incurs compute cost

Analyst 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 views

AI-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 prompts

AI-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 you

Experimentation:Identify small improvement ideas, build prototypes and present them during sprint reviews. The focus is on learning rather than measurable impact

Must-haves:

• Final-year student or recent graduate in Computer Science, Information Technology or a related field, or equivalent practical experience
• OLTP vs OLAP systems and their respective use cases
• Ability to write correct, readable SQL, including joins, aggregations, filters and basic window functions
• Working knowledge of Python; familiarity with Scala or Java is a plus

Ability to read a table schema and explain the data it represents

Basic understanding of how LLM / GPT models work, including tokens, context windows, prompting and the causes of hallucination

Practical experience using AI coding assistants for development tasks, with the judgement to verify their output

At least one academic project or internship involving data or backend engineering that you can explain in detail

Proficiency with Git, Linux and the command line

A curious mindset: you question unexpected data patterns and elevate issues early

Good to have

• Exposure toApache Spark, Airflowor any data processing or orchestration framework
• Exposure to BI tools such asSuperset, Metabase or Power BI
• Interest infintech, payments

We 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.