Data Engineer

Are you a Data Engineer (5-7+ years) who enjoys owning production-grade pipelines end-to-end, optimising performance, and working with modern Python tooling on time-series datasets?

I'm supporting a London-based fintech in their search for a hands-on 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 small engineering/analytics team and take ownership of pipelines end-to-end - from onboarding new datasets through to reliability, monitoring and data quality in production. Finance experience is a bonus, but not essential.

Note: they use cloud infrastructure but deploy services on their own servers, so a strong production/ops mindset is important.

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 columnar stack (Polars + Parquet/Arrow/PyArrow; DuckDB is a nice-to-have)
• Orchestrate workflows and improve reliability (they use Temporal - similar orchestration experience is fine)
• Own production readiness: validations, automated checks, backfills/reruns, monitoring/alerting, incident/RCA mindset
• Work independently and help drive delivery forward - including providing practical technical guidance to shape solutions

What's in it for you?

• Modern Python stack - Polars + Parquet/Arrow (DuckDB a plus)
• Ownership & impact - high visibility; you'll influence performance and reliability directly
• Market/time-series exposure - complex financial datasets; learn the domain as you go
• Hybrid London - London preferred, 2-3 days in the office
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• 5-7+ years hands-on data engineering experience
• Strong Python + SQL fundamentals (ETL, pipelines, data modelling, performance)
• Hands-on experience with Polars and Parquet/Arrow/PyArrow
• Proven ability to operate pipelines in production (monitoring, backfills, data quality, incidents)
• Able to work independently and drive things forward without heavy oversight
• Interest in financial data (experience helpful but not required)

Nice to have:

• DuckDB experience
• Time-series experience (market data, telemetry, pricing, events)
• Streaming exposure (Kafka/Event Hubs/Kinesis)
• Experience with Temporal (or similar orchestrators like Airflow/Dagster/Prefect)
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