Data Engineer Founding Role in London - eFinancialCareers

Location: Remote | Employment: Full-time | Language: English

We’re looking for our first Data Engineer — someone ready to build and own the foundation of our data infrastructure from the ground up. You’ll take full ownership of critical datasets, from ingestion and system design to reliability, accessibility, and performance.

You’ll architect, build, and operate the data backbone that powers our algorithmic and research teams — transforming messy external feeds into clean, high-performance datasets that drive insights and decisions.

What You’ll Do

• • Build from scratch: Design and implement cloud-native batch and streaming ELT pipelines for diverse data sources.
  • Create robust systems: Architect storage and lakehouse solutions, orchestration, metadata/cataloging, CI/CD, IaC, and observability — all kept simple, reliable, and cost-efficient.
  • Ensure data integrity: Develop data quality checks, anomaly detection, and bias-free historical data handling (including corporate actions and entitlements).
  • Deliver usable data: Provide clean, well-documented datasets through APIs, query layers, and shared libraries — optimized for both research and production.
  • Collaborate deeply: Work side-by-side with quants, data scientists, and software engineers to scope, prototype, and productionize datasets quickly.
  • Operate with discipline: Manage incident response, maintain clear runbooks, and uphold strong data security practices (IAM, least privilege, audit, and secrets management).

What You’ll Bring

• • 1+ years building and maintaining production-grade data pipelines or platforms (or equivalent experience).
  • Strong Python and SQL skills, plus familiarity with distributed, time-series, or NoSQL databases.
  • Experience on at least one major cloud platform (AWS, GCP, or Azure).
  • Practical knowledge of Docker and Terraform (or similar IaC tools).
  • Hands-on experience with orchestration tools (Airflow, Prefect, Dagster) and distributed/batch compute frameworks (Spark, Dask, Beam).
  • Familiarity with modern data formats (Parquet, Delta, Iceberg) and data warehouses/lakehouses.
  • Comfort with monitoring, observability (logs/metrics/traces), and cost optimization.
  • Proven ability to deliver for quantitative, ML, or research-focused teams — with clear thinking and pragmatic engineering trade-offs.

Bonus Points For

• • Experience handling financial or time-series data , including corporate actions, entitlements, or alternative data sources.
  • Exposure to multimodal ETL (e.g., NLP, embeddings, transcription, image/video data).
  • Familiarity with dataset versioning and reproducibility tools (LakeFS, DVC) or research workflow automation.

This is a rare opportunity to shape a data platform from day one — working directly with world-class researchers and technologists to turn raw data into actionable intelligence.