BettingJobs 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.

Responsibilities:

• 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

Requirements:

• 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 PostgreSQL
• Proven experience preparing, transforming and validating datasets for analytical, modelling or research use cases
• 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 ClickHouse, BigQuery, Snowflake or Redshift (beneficial)
• Experience with version control systems (preferably GitLab) 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