Data Engineer

Description

The company is one of Europe's leading betting and gaming operators. It runs a broad portfolio of well-known brands, has more than 5,000 employees, and operates in around fifteen regulated markets. Its games include both exclusive-rights titles and open-competition titles, and it puts responsible play and safety first.

The role focuses on the sportsbook product. You will help build and extend a next-generation data platform that delivers reliable, scalable data close to real time across several brands and markets.

The sportsbook data platform is an event-driven lakehouse on AWS, organised around medallion architecture. It supplies curated data for analytics, trading insight and machine learning, and supports decisions on trading, risk, personalisation and analytics across the company.

Responsibilities

• Build and maintain batch and streaming pipelines for ingestion, transformation and serving.
• Create and improve data assets in the bronze, silver and gold layers, keeping them accurate and easy to use.
• Turn sportsbook data on bets, offers, rewards and digital activity into clean, well-structured datasets for other teams.
• Write transformation logic with modern tools such as dbt, Spark or SQL-based frameworks, favouring clear and dependable code.
• Help build streaming pipelines on Kafka, Flink or similar tools to serve near real-time use cases.
• Run data quality checks and validation to keep data accurate and consistent.
• Help define and put in place data contracts and schemas so systems integrate reliably.
• Watch pipelines, troubleshoot issues, and keep them fast, reliable and cost-effective on AWS.
• Work with analytics, data science and machine learning teams to deliver data solutions that meet business needs.
• Support data governance through documentation, metadata and easier dataset discovery.

Requirements

• Two to four years of experience in data engineering or a related field.
• Strong SQL and a solid grasp of data modelling.
• Experience with transformation tools such as dbt or Spark, and with orchestration tools such as Airflow or similar.
• Familiarity with cloud data platforms, ideally AWS.
• Understanding of pipeline design and ETL or ELT patterns.
• Exposure to streaming technologies such as Kafka or Flink is beneficial.
• Awareness of data quality, testing and monitoring practices.
• Ability to work with stakeholders to gather requirements and deliver data solutions.
• Willingness to learn and grow in a fast-paced, changing environment.
• Exposure to sports betting, trading or financial data.
• Familiarity with event-driven architectures.
• Experience with semantic layers or BI tools.
• Exposure to data governance or metadata tooling.
• Interest in supporting machine learning or analytics workflows.

Conditions

Applicants are assessed only on skills, experience and potential, regardless of age, gender, origin, religion, sexual orientation, neurodiversity or disability.