Data Engineer

Job description

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!

Extra information

Status

Open

Education Level

Secondary School

Location

London Area

Type of Contract

Full-time jobs

Published at

18-02-2026

Profession type

Accountancy

Full UK/EU driving license preferred

No

Car Preferred

No

Must be eligible to work in the EU

No

Cover Letter Required

No

Languages

English