Internship programs

Move data between systems without losing, corrupting or silently duplicating any of it. Over 12 weeks you'll plan and execute migrations end to end: profiling the source, mapping schemas, handling the rows that do not fit, reconciling counts, and designing a rollback you would actually be willing to use. Migrations are judged entirely on what does not happen. The work is in the verification — proving that what arrived matches what left, and knowing what to do about the three percent that never maps cleanly. Topics covered: source profiling and data quality assessment, schema mapping and type coercion, incremental and dual-write cutover strategies, idempotent and resumable loads, reconciliation and row-level verification, rollback planning, zero-downtime cutover.

Turn raw data into decisions a product team will actually act on. Over 10 weeks you'll work with real business datasets, write production-quality SQL, build dashboards stakeholders will use weekly, and present findings to product and engineering owners. The work is half technical, half communication. You'll learn the SQL patterns that scale, the BI conventions that prevent stale numbers, and the storytelling structure that gets your insight onto the roadmap. Topics covered: advanced SQL, exploratory data analysis, dashboarding (Metabase / Looker patterns), A/B test analysis, cohort and funnel analysis, executive communication, statistical fundamentals.

Build the data pipelines that every analyst, ML engineer, and product team depends on. Over 12 weeks you'll design and ship ETL/ELT flows, work with cloud data warehouses, handle schema evolution gracefully, and operate both batch and streaming workloads on real datasets. By the end you'll be comfortable writing dbt models a senior engineer would merge without hesitation, debugging Airflow DAGs at 2 a.m., and explaining why your warehouse bill went up this month. Topics covered: ETL/ELT design, SQL fluency, dbt, Apache Airflow, data warehousing (Snowflake / BigQuery / Redshift patterns), streaming basics (Kafka), data quality testing, cost optimisation.

Build production-grade AI features end-to-end. Over 12 weeks you'll design prompts, integrate large language models with tools and retrieval, write evaluations that catch regressions before users do, and ship a small AI-assisted application with real cost and safety controls. By the end you'll have a working portfolio piece, a mental model for when LLMs are the right hammer (and when they aren't), and the muscle memory to debug AI systems in production — not just demo them. Topics covered: prompt engineering, structured outputs, tool use, RAG (retrieval-augmented generation), evaluation frameworks, cost optimisation, safety and red-teaming, observability for AI systems.