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We're looking for an Data Engineer Intern specializing in data transformation to join Malt's Data Platform team for 6 months, starting September 2026. You'll work closely with a Senior Analytics Engineer who will support and guide you throughout your internship.
This is not a passive internship. You'll contribute to real production work across three tracks: helping research and implement a context layer that makes our data AI-ready, building AI agents that make our data team faster, and tackling legacy table debt that has been slowing us down. You'll also collaborate with product engineers to improve how we instrument and observe our platform because good agents start with good data.
Contribute to delivering a context layer for AI-readiness. With guidance from your manager.
Contribute to implementing the context layer strategy. Once the proposal is validated, you'll help implement and test it with AI tools to assess effectiveness.
Help remove legacy tables. Identify unused or outdated tables, coordinate with stakeholders to confirm it's safe to deprecate, and clean them up. Less glamorous, but it directly unblocks the team.
Build and iterate on AI agents within the data engineering codebase (e.g. dbt model generators, SQL reviewers, documentation writers) to reduce repetitive work supported by senior engineers.
Collaborate with engineering, analytics, and business teams.
Document your work: architecture decisions, data model patterns, deprecation rationale, and agent skills so the team can build on it after your internship ends.
High-level education ideally in Computer Science, Data Engineering, or a related field (BAC+4/5); final-year students welcome
You build, not just spec: comfortable writing SQL, building dbt models, and scripting in Python
You can research a problem, form a point of view, and present a structured recommendation not just execute tasks handed to you
You follow AI developments (LLMs, agents, RAG) and have hands-on experience with at least one framework: OpenAI API, LangChain, or similar — you understand why data quality is the foundation of any good AI system
Strong data quality mindset: you know a KPI without a baseline is just a number, and you care about lineage, documentation, and reproducibility
Autonomous and proactive: you flag blockers early and thrive in a fast-moving environment with multiple concurrent projects
Professional working proficiency in English (written and verbal)
Screening call with HR (30 min) to understand your background, motivations, and answer any questions
1st interview with your future manager (60 min)
Homework: a short take-home exercise to present during the 3rd interview