🚀 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!