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Sportradar's Ads Analytics team is the backbone of their advertising intelligence platform, processing terabytes of data daily from programmatic, social, and search advertising channels. They transform raw advertising data into actionable business insights and create sophisticated sport audience segments.
You will take ownership of complex, multi-faceted data projects at scale. Key responsibilities include:
**Scale & Performance Engineering**: Process and analyze terabytes of advertising data with sub-second query performance. Build and maintain robust ETL pipelines using Spark and AWS services to handle massive daily data volumes.
**Data Pipeline Architecture & Development**: Design and build scalable data processing systems. Develop backend APIs and microservices in Python or Go. Architect data flows supporting both batch and real-time analytics. Manage user-facing dashboards visualizing complex data insights.
**Infrastructure & Data Quality Operations**: Implement robust monitoring and alerting systems to detect data quality issues. Manage AWS infrastructure using Terraform. Implement CI/CD best practices and maintain high coding standards across data processing systems.
**Cross-Functional Leadership & Collaboration**: Lead large-scale data projects from requirements gathering through delivery. Bridge technical implementation with business requirements. Mentor team members and present technical concepts to stakeholders while challenging requirements constructively.
**End-to-End Data System Ownership**: Take complete ownership of complex data engineering projects ensuring high availability and accuracy for internal stakeholders and external clients. Champion clean code principles and serve as a knowledge leader.
Sportradar offers a collaborative global environment with engineering offices in Europe, Asia, and the US. They provide professional development plans, career growth opportunities, vibrant inclusive community (Women in Tech, Pride groups), company culture promoting sports and wellness, competitive salary and benefits including retirement pension and insurance, and full coverage of the Vienna public transport annual ticket (€365).
The role is fully office-based in Vienna, as the company strongly values in-person collaboration, knowledge sharing, fast decision-making, and team connection.
**Requirements:**
- 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
- Beneficial: Experience with GenAI tools and contextual/prompt engineering (GitHub Copilot, Claude, or similar)
