Data Engineer - Quantitative Analysis

BettingJobs; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

BettingJobs is seeking a Data Engineer to join a small but growing quant team in sports betting. You will ensure reliable, well-structured data for research and modelling, building robust Python workflows and investigating data issues to derive maximum value.

Collaborating with modelling and engineering, you will validate datasets, improve data flows and contribute to scalable data solutions for analytical use across the team.

Qualifications

• Experience as a Quant Data Engineer, Research Data Engineer or similar with complex datasets.
• Proficiency in Python for data processing, investigations and workflows.
• Strong SQL skills and relational database experience (PostgreSQL preferred).
• Experience building and maintaining data pipelines in production environments.
• Understanding of data quality, reconciliation and validation practices.
• Experience with analytical data warehouses (ClickHouse/BigQuery/Snowflake/Redshift) is a plus.

Responsibilities

• Work with quant modellers to prepare and maintain datasets for research and modelling.
• Investigate data issues, identify root causes and coordinate resolution.
• Build and maintain Python-based data workflows for ingestion, transformation and validation.
• Maintain historical data assets to ensure accuracy and accessibility for analysis.
• Improve upstream/downstream data flows and ensure critical data is captured.

Skills

Python

SQL

PostgreSQL

Data Pipelines

Data Quality

Agile

Strong analytical skills

Excellent communication

Tools

GitLab

JIRA

Confluence

ClickHouse

BigQuery

Snowflake

Redshift

Job description

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