Data Engineer

At TMLC, the model is only as good as the data feeding it. As a Data Engineer, you'll own the layer that makes our enterprise AI dependable — designing and operating the pipelines, storage and feature infrastructure that every GenAI, ML and computer-vision system we ship relies on.

You'll work shoulder-to-shoulder with AI and GenAI engineers, translating raw, often-messy client data into clean, well-modelled, well-governed assets. This is hands-on engineering with real ownership — your work goes live inside production enterprise environments.

What you'll do

• Design, build and maintain batch and streaming data pipelines (ELT/ETL) that move enterprise data reliably at scale.
• Model and operate data lakes and warehouses on cloud platforms, with sensible schemas and partitioning.
• Build and maintain feature stores and datasets that ML and GenAI engineers can trust and reuse.
• Implement data quality, validation and observability so issues are caught before they reach a model.
• Integrate securely with enterprise source systems — databases, APIs, document stores and event streams.
• Champion governance, lineage and security for sensitive client data from day one.

What we're looking for

• 2–5 years building production data systems, with strong Python and SQL.
• Hands-on with orchestration (Airflow / Dagster / Prefect) and distributed processing (Spark).
• Comfortable on at least one cloud (AWS, GCP or Azure) and modern warehouses (BigQuery, Snowflake, Redshift).
• Solid data modelling instincts and a bias toward reliability, testing and clean code.
• Clear communicator who can work directly with clients and a small senior team.

Bonus points

What we offer

• Competitive compensation aligned with impact
• Work alongside Kaggle Grandmasters and top AI researchers on frontier problems
• Real ownership and surface area — outcomes, not tickets
• Hybrid flexibility and a learning-first culture via TMLC Academy