Data Engineering Intern

About this role

Batch: 2025/2026/2027. About the role: - Build and maintain data pipelines that power Fam's platform, working with both transactional and analytical data systems. - Write SQL queries to extract, transform, and load data across different schemas and tables. - Develop Python scripts and tools to automate data processes and solve engineering problems. - Debug data issues by spotting anomalies, unexpected patterns, and inconsistencies in datasets. - Collaborate with engineers and stakeholders to understand requirements and deliver solutions on time. What you'll work on: - Designing and optimizing queries using joins, aggregations, filters, and window functions to meet business needs. - Understanding the difference between OLTP and OLAP systems and choosing the right approach for each task. - Learning data orchestration frameworks and scheduling tools to automate recurring workflows. - Working with modern AI coding assistants as productivity tools while maintaining code quality through careful review. - Reading and interpreting database schemas to grasp what data exists and how it connects. What we're looking for: - Pursuing or recently completed a degree in Computer Science, Information Technology, or an equivalent field. - Solid SQL skills: you can write queries involving joins, aggregations, filters, and basic window functions without breaking a sweat. - Working knowledge of Python; experience with Scala or Java is a bonus. - Hands-on experience with Git, Linux, and the command line. - At least one data engineering or backend-focused academic project or internship on your resume. - Basic familiarity with how LLMs and GPT models work: tokens, context windows, prompts, and the concept of hallucinations. - Comfort using AI coding assistants and the ability to verify and validate their output before using it. - The ability to read a database table schema and explain what data it holds. - A curious, detail-oriented mindset that naturally spots when data looks off. - Clear communication skills and the judgment to raise concerns early rather than waiting. Good to have: - Experience with Apache Spark, Airflow, or similar data processing and orchestration frameworks. - Knowledge of idempotency, partitioning strategies, and columnar storage formats like Parquet. - Ability to read query execution plans and spot inefficiencies like full table scans. - Hands-on experience running open-weight LLMs on your own machine using Ollama, llama.cpp, or equivalent tools. - Previous work building embeddings or similarity search prototypes. - Understanding of retrieval-augmented generation (RAG), including how chunking, embeddings, retrieval, and grounding work together. - Exposure to vector databases such as pgvector, Qdrant, Chroma, or OpenSearch. - Familiarity with AWS basics like S3 and IAM. - Experience with analytics and BI platforms such as Superset, Metabase, or Power BI. - Genuine interest in fintech, payments infrastructure, or data privacy topics.