Data Engineer
🚀 Are you a Data Engineer who enjoys building production-grade pipelines, optimising performance, and working with modern Python tooling (DuckDB/Polars) on time-series datasets
I’m supporting a UK-based fintech in their search for a hands-on Python Data Engineer to help build and improve the data infrastructure powering a unified data + analytics API for financial markets participants.
You’ll sit in a engineering/analytics team and take ownership of pipelines end-to-end — from onboarding new datasets through to reliability, monitoring and data quality in production.
In this role, you’ll:
- 🔧 Build, streamline and improve ETL/data pipelines (prototype → production)
- 📈 Ingest and normalise high-velocity time-series datasets from multiple external sources
- ⚙️ Work heavily in Python with a modern stack including DuckDB and Polars (plus Parquet/PyArrow)
- 🧩 Orchestrate workflows and improve reliability (they use Temporal — similar orchestration experience is fine)
- ✅ Improve data integrity and visibility: validations, automated checks, backfills, monitoring/alerting
- 📊 Support downstream analytics and client-facing outputs (dashboards/PDF/Plotly — least important)
What’s in it for you?
- 📌 Modern data stack – DuckDB/Polars + Parquet/Arrow in a genuinely hands-on environment
- 📈 Ownership & impact – You’ll be close to the data flows and have real influence on performance and reliability
- 🏦 Market data exposure – Work with complex financial datasets (experience helpful, interest is enough)
- 🏢 Hybrid London – London preferred, with 2–3 days in the office
- ⚡ Start ASAP – Interviewing now
What my client is looking for:
- Strong Python + SQL fundamentals (data engineering / ETL / pipeline ownership)
- Hands-on experience with DuckDB and/or Polars (DuckDB especially valuable)
- Experience operating pipelines in production (monitoring, backfills, incident/RCA mindset, data quality)
- Cloud experience with demonstrable production use (Azure preferred)
- Clear communicator, comfortable working across engineering/analytics stakeholders
Nice to have:
- Time-series data experience (market data, telemetry, pricing, events)
- Streaming exposure (Kafka/Event Hubs/Kinesis)
- Experience with Temporal (or similar orchestrators like Airflow/Dagster/Prefect)
- Any exposure to AI agents / automation tooling
👉 Apply now!