Posted:1 week ago| Platform: Linkedin logo

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Job Type

Full Time

Job Description

A UK based Hedge fund in looking for a Data Engineer for their Data team to architect and build the tools used to create new data-sources and monitor key production systems. This role offers hands-on exposure to the core datasets, and will partner closely with other technology teams, quants, and traders, building systems that guarantee the integrity of our data and avoid trading losses.

Responsibilities:

  • Data Quality Tools

  • Your key deliverable is to develop and maintain a platform which ensures the quality of our data-pipelines, and alerts stakeholders to critical production issues.
  • Library Maintenance

  • Maintain and improve shared libraries which are used by developers across the data-function and facilitate the rapid onboarding of new datasets.
  • Cross-team Integrations

  • Design, build, and document our integrations with other team’s processes, which alert them to relevant issues with our data-pipelines.
  • Observability & Monitoring

  • Build out the observability stack (logs, metrics, tracing) to monitor our data assurance system, as well as other tools for the management of our data-pipeline lifecycle.
  • Platform Support

  • Support users in best practice usage of our tooling, migrate code to the latest standards as well as investigate, debug and resolve issues with our platform.


Experience:

  • 5+ years of hands-on experience in data engineering or data-adjacent engineering roles
  • Strong software engineering fundamentals and proficiency in Python
  • Experience with workflow orchestration frameworks (e.g. Argo, Celery, Airflow)
  • Experience designing and implementing RESTful APIs and internal service integrations
  • Experience with observability tools (e.g. Prometheus, Grafana, CloudWatch)
  • Familiarity with Kubernetes, container orchestration, and cloud infrastructure (AWS preferred)
  • Exposure to the front-end, particularly with regards to custom monitoring dashboards, is a plus
  • Knowledge of data-quality frameworks (Soda/Great Expectations) is a plus
  • Knowledge of ETL/ELT pipelines, and metadata management is a plus

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