Senior Data Engineer

We've had strong results with a profile that blends hands-on software/data engineering with direct client delivery - someone who embeds with a client team, understands their commercial problem, and builds and ships the tooling to solve it. We want more of this, at a senior / lead level: someone who can own the client relationship, scope ambiguous problems independently, and guide more junior engineers, not just execute a defined spec.A computational scientist / forward deployed engineer who can move fluidly between data pipelines, backend logic, optimization/simulation, and client-facing dashboards - and deploy it all to production. The defining trait is the combination of genuine engineering depth with the maturity to sit in front of clients, translate evolving stakeholder needs into features, and drive delivery.Strong Python for data and backend work - Pandas plus optimization/scientific libraries (e.g. Pyomo, Scikit-Learn)Building simulation, forecasting, and KPI/analytics frameworks to model financial or operational outcomesData engineering: ingestion and transformation pipelines for time-series / market data, schema design, automated validation/testing (e.g. pytest)Interactive dashboarding (e.g. Plotly Dash, Bokeh, or similar) for decision supportCloud and deployment: AWS, Docker, CI/CD across dev/QA/prod; message queues (e.g. RabbitMQ) a plusDatabases: PostgreSQL, plus exposure to NoSQL (e.g. Cassandra) and OracleOwns client relationships and runs the cadence (regular working sessions, requirements gathering, alignment)Scopes loosely defined problems into deliverable workstreams with minimal directionCan technically lead on a delivery teamWorking with non-technical stakeholders, problem framing mindset.Advanced degree in a quantitative field (statistics, analytics, computer science, engineering or similar)Able to collaborate across European and North American time zonesEnglish essential; Spanish a strong plus

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