Selected work
Engineering in context.
A closer look at my contributions, the problems they addressed and the lessons behind the implementation.
These are high-level professional experience summaries. Proprietary code, internal architecture and unverified metrics are not published.
When successful pipelines produce incorrect data.
Engineering for correctness in financial regulatory reporting: transformations, validations and the business meaning behind the output.
Spark / SQL / Databricks
Keeping banking data moving.
Real-time consumers, incremental ingestion and orchestration across relational and document databases at blu Bank.
Scala / Python / Kafka / Airflow
Making data engineering easier to deliver.
CI improvements, extraction configurations, internal tools and knowledge sharing that support day-to-day engineering.
CI / Configuration / Documentation
Let’s work together
Have a data challenge
or a role in mind?
Let’s discuss the problem, the team and where I could contribute.