Data Engineer

What You’ll Work On

Your Experience

• 2–4 years experience in data engineering or a related field.
• Strong SQL skills and a good understanding of data modelling principles.
• Experience with data transformation tools (e.g. dbt, Spark) and workflow orchestration (e.g. Airflow or similar).
• Familiarity with cloud-based data platforms, ideally AWS.
• Understanding of data pipeline design and ETL/ELT patterns.
• Exposure to streaming technologies (Kafka, Flink, or similar) is beneficial.
• Awareness of data quality, testing, and monitoring practices.
• Ability to work with stakeholders to understand requirements and deliver data solutions.
• Willingness to learn and grow in a fast-paced, evolving environment.

Nice to Have

• Exposure to sports betting, trading, or financial data domains.
• Familiarity with event-driven architectures.
• Experience with semantic layers or BI tooling.
• Exposure to data governance or metadata tooling.
• Interest in supporting machine learning or analytics workflows.
• Why This Role Matters
• This role contributes to building the trusted data foundation that powers analytics, trading insight, and machine learning across sportsbook. By delivering reliable and well-structured data assets, you’ll help enable better decision-making and support the continued evolution of our scalable, modern data platform.
• We believe talent knows no boundaries. Our hiring process focuses solely on your skills, experience, and potential to contribute to our team. We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics as the age, gender, origin, religion, sexual orientation, neurodiversity or disability.

Why This Role Matters

Responsibilities

• Build and maintain batch and streaming data pipelines, supporting ingestion, transformation, and serving layers.
• Develop and enhance data assets aligned to medallion architecture (bronze, silver, gold), ensuring quality and usability.
• Transform sportsbook domain data (bets, offers, rewards, digital data) into well-structured datasets for downstream consumption.
• Implement data transformation logic using modern tooling (e.g. dbt, Spark, SQL-based frameworks) with a focus on clarity and reliability.
• Contribute to streaming data pipelines (Kafka/Flink or equivalent) to support near real-time data use cases.
• Apply data quality checks and validation to ensure accuracy and consistency of data assets.
• Support the implementation of data contracts and schemas for reliable integration between systems.
• Monitor and troubleshoot pipelines to ensure performance, reliability, and cost efficiency on AWS.
• Collaborate with analytics, data science, and machine learning teams to deliver data solutions aligned to business needs.
• Contribute to data governance practices, including documentation, metadata, and dataset discoverability.
• A modern sportsbook data platform built on AWS, supporting both real-time and batch data processing.
• Medallion-aligned data layers transforming raw data into curated, business-ready datasets.
• Data pipelines processing sportsbook events such as bets, pricing, and settlements.
• Data assets supporting trading analytics, risk monitoring, and customer personalisation.
• Integration with BI tools, semantic layers, and machine learning platforms.
• Data governance and metadata tooling to improve transparency and trust in data.