• Build the MLOps Platform that powers credit risk scoring, automated lending decisions, and transaction categorisation. This platform supports the full ML lifecycle from feature engineering to production inference

• Establish platform-wide conventions for data modelling, data contracts, and reusable pipelines, while driving performance and cost optimisations for heavily computational Spark jobs

• Architect and roll-out platform standards like ingestion frameworks, data modelling conventions, data contracts, and ML operations patterns (feature stores, model registry, reusable training/inference pipelines).

• Architect CI/CD infrastructure, shared tooling, and multi-environment deployment strategies across a growing multi-repo ecosystem

• Ensure our infrastructure serves as a reliable, scalable product for internal teams to ultimately deliver optimal service to our customers.

• Propose improvements to our AI-assisted development workflow setup; think of agents, skills and AI driven integrations.

• Strengthen data governance, access controls, security practices, and SDLC maturity. Support data science teams in adopting these standards

• Actively identify cross-departmental opportunities for platform improvements

What you bring

• 7+ years of experience as a Data Engineer, Platform Engineer or ML(Ops) Engineer

• Bachelor’s or Master’s degree in Computer Science, Engineering or another technical field

• Strong experience with Python and/or PySpark, and solid SQL skills

• Familiarity with Databricks and Databricks Asset Bundles (DABs)

• Experience with orchestration tools like Databricks Lakeflow Jobs, Airflow or Dagster

• Expertise in building multi-environment CI/CD deployment strategies across a multi-repo ecosystem

• Hands-on experience with unit and integration testing

• Understanding of containerisation techniques such as Docker

• Familiarity with data architecture and system design

• A proven track record of navigating cross-department environments, driving alignment, and executing technical solutions end-to-end; from ideation to production.

• The ability to translate complex technical concepts for non-technical peers and proactively mentor data teams to adopt mature governance standards.