Regulatory Data Engineer — BigQuery & Cloud Pipelines

bet365; Stoke-on-Trent; On-site; GBP 65,000 - 90,000; Full time

Job summary

bet365 is seeking a Regulatory Data Developer to shape reporting systems for regulatory submissions and third‑party reporting. You will migrate legacy SQL Server solutions to GCP, design cloud-native outputs in BigQuery, and ensure accuracy, traceability, and performance.

Responsibilities include building ETL/ELT pipelines, implementing validation and monitoring, and delivering scalable data solutions with AI-assisted tooling while maintaining thorough documentation and code quality.

Qualifications

• Strong Google BigQuery experience.
• Advanced SQL development skills, including complex queries and performance tuning.
• Experience building ETL/ELT data pipelines.
• Familiarity with Terraform and CI/CD tooling.
• Exposure to CI/CD pipelines and version control such as Git/GitLab.

Responsibilities

• Develop and maintain regulatory data submission systems across SQL Server and BigQuery.
• Build automated pipelines for ingesting, transforming and loading regulatory data.
• Migrate legacy reporting solutions to BigQuery through data modelling and delivery.
• Implement validation checks, monitoring and alerting for submission accuracy.
• Collaborate with seniors to shape cloud-native reporting solutions.
• Apply AI tooling to reduce manual effort and improve delivery quality.
• Produce clear documentation for technical processes and designs.
• Participate in code reviews and follow development standards.

Skills

BigQuery

Advanced SQL

ETL/ELT pipelines

AI tooling awareness

CI/CD

Tools

Cloud Storage

Cloud Functions

Pub/Sub

Cloud Composer

Terraform

Git/GitLab

SQL Server

Job description

bet365 is seeking a Regulatory Data Developer to shape reporting systems for regulatory submissions and third‑party reporting. You will migrate legacy SQL Server solutions to GCP, design cloud-native outputs in BigQuery, and ensure accuracy, traceability, and performance.

Responsibilities include building ETL/ELT pipelines, implementing validation and monitoring, and delivering scalable data solutions with AI-assisted tooling while maintaining thorough documentation and code quality.