Job Description
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
* Start ASAP – interviewing now
What my client is looking for:
* 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)
* Any exposure to AI agents / automation tooling
Apply now!