Senior Software Engineer - Data (Python, AWS)
The Ads Analytics team is the backbone of our advertising intelligence platform, processing terabytes of data daily from programmatic, social, and search advertising channels. We build end-to-end solutions that transform raw advertising data into actionable business insights and leverage rich analytics data to create sophisticated sport audience segments.
We're scaling rapidly and need a senior data engineer who can take ownership of complex, multi-faceted data projects. You'll be tackling challenges like:
Scale & Performance Engineering : Processing and analyzing terabytes of advertising data with sub-second query performance while building and maintaining robust ETL pipelines using Spark and AWS services to handle massive data volumes daily
Data Pipeline Architecture & Development : Designing and building scalable data processing systems, developing backend APIs and microservices (Python or Go), architecting data flows that support both batch and real-time analytics requirements, and managing user-facing dashboards that visualize complex data insights
Infrastructure & Data Quality Operations : Implementing robust monitoring and alerting systems to detect data quality issues, managing AWS infrastructure using Terraform, implementing CI/CD best practices, and maintaining high coding standards across data processing systems
Cross-Functional Leadership & Collaboration : Leading large-scale data projects from requirements gathering through delivery, bridging technical implementation with business requirements, mentoring team members, and presenting technical concepts to stakeholders while challenging requirements constructively
End-to-End Data System Ownership : Taking complete ownership of complex data engineering projects while ensuring high availability and accuracy for both internal stakeholders and external clients, championing clean code principles, and serving as a knowledge leader who supports delivering the right data solutions
- Processing and analyzing terabytes of advertising data with sub-second query performance while building and maintaining robust ETL pipelines using Spark and AWS services to handle massive data volumes daily
- Designing and building scalable data processing systems, developing backend APIs and microservices (Python or Go), architecting data flows that support both batch and real-time analytics requirements, and managing user-facing dashboards that visualize complex data insights
- Implementing robust monitoring and alerting systems to detect data quality issues, managing AWS infrastructure using Terraform, implementing CI/CD best practices, and maintaining high coding standards across data processing systems
- Leading large-scale data projects from requirements gathering through delivery, bridging technical implementation with business requirements, mentoring team members, and presenting technical concepts to stakeholders while challenging requirements constructively
- Taking complete ownership of complex data engineering projects while ensuring high availability and accuracy for both internal stakeholders and external clients, championing clean code principles, and serving as a knowledge leader who supports delivering the right data solutions
- 5+ years of data engineering experience with proven track record of leading complex data projects from conception to delivery
- Exceptional communication skills and experience working in cross-functional teams with analysts, product managers, and business stakeholders
- Very strong hands-on experience with AWS services (S3, Lambda, Glue, Athena, Redshift, EMR, etc.) and proficiency with Apache Spark for large-scale data processing
- Strong experience with Python for building data processing services and APIs, plus expert-level SQL for data processing and analytics
- Hands-on experience with Docker, Terraform, and CI/CD pipelines with automation best practices for data systems
- Strong commitment to writing clean, maintainable, well-documented code with comprehensive testing and deep knowledge of analytics/reporting requirements
- Experience designing scalable data architectures, data modeling, and optimizing data processing workflows
- Experience creating and managing analytics dashboards in bi tools (Tableau, Qlik Sense, Quicksuite, Power BI) and data visualization solutions to present complex insights to stakeholders
- GenAI Tools (beneficial): Experience working with contextual and prompt engineering with GitHub Copilot, Claude, or similar AI assistants
- A collaborative environment with colleagues from all over the world (Engineering offices in Europe, Asia and US)
- Ability to shape your own workday and career via a clearly defined professional and personal development plan
- Opportunity to work with senior leadership, develop yourself and build your career within an inspiring and fast-growing company and digital sports environment.
- A vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants
- A company culture that promotes social aspects, sports, physical exercise and fun.
- Innovative and cross-team challenges like ShipIt, office sports tournaments in Darts and Table Tennis and unique beer brewing competitions.
- Competitive salary and benefits (e.g. retirement pension and insurance plan)
- Sportradar takes over the full costs of € 365.- for the Öffi-Ticket (Jahreskarte) for you
Sportradar is a global sports technology company that collects, analyses and distributes sports data to betting operators, sports leagues and media companies. Headquartered in St. Gallen, Switzerland, and founded in 2001, it provides betting services, managed trading, integrity monitoring and audiovisual streaming across hundreds of sports. The company holds official data partnerships with organisations including the NBA, NHL, FIFA and ICC. Sportradar is listed on the Nasdaq stock exchange and employs several thousand people worldwide.
