Data Engineer - Quantitative Analysis

Betting Jobs is seeking a Data Engineer to join a small but growing quant team in the sports betting industry.

Working alongside the modelling team, you will be responsible for ensuring they have access to reliable, well‑structured and high‑quality data for research, modelling and analysis. From building robust Python‑based workflows to investigating complex data issues and assessing new data sources, the Data Engineer will be responsible for extracting maximum value from the data.

• Work day‑to‑day with quant modellers to prepare, refine and maintain datasets used for research, modelling and analysis.
• Investigate data issues affecting modelling outputs, identifying root causes and working with relevant teams to resolve them.
• Build and maintain Python‑based data workflows and pipelines for ingestion, transformation and validation of modelling data.
• Maintain and develop historical data assets, ensuring they remain accurate, accessible and fit for analytical use.
• Work with engineers to improve upstream and downstream data flows, ensuring critical data is captured and processed effectively.
• Ensure data quality and integrity through validation, reconciliation and targeted monitoring across key datasets.
• Expand visibility into data issues by improving checks, alerts and investigative workflows across critical pipelines.
• Define and improve data logic, transformations and assumptions, ensuring they are clearly documented and consistently applied.
• Support data migrations, backfills and structural improvements to improve the reliability of modelling datasets.
• Contribute to tooling and processes that make it easier to explore, prepare and troubleshoot data used by the quant team.

• Strong experience in a Quant Data Engineer, Research Data Engineer or similar role working with complex datasets.
• Understanding of the sports betting industry.
• Strong Python skills for data processing, investigation and workflow development.
• Excellent SQL skills and solid experience with relational databases, preferably Postgre
  
  SQL.
• Proven experience preparing, transforming and validating datasets for analytical, modelling or research use.
• Experience investigating data issues and tracing problems through pipelines, transformations and source systems.
• Experience building and maintaining data pipelines or processing workflows in production environments.
• Strong understanding of data quality, reconciliation and validation practices.
• Experience working with analytical data warehouse technologies such as Click House, Big Query, Snowflake or Redshift (beneficial).
• Experience with version control systems (preferably Git Lab) and tools such as JIRA and Confluence.
• Comfortable working with messy, incomplete or evolving datasets and turning them into reliable assets.
• Experience working in Agile environments and collaborating with distributed teams.
• Excellent attention to detail, strong problem‑solving ability and clear verbal and written communication skills.