Role Summary
As Director, you will lead a high-performing team responsible for building and operating scalable data platform components within Deutsche Telekom's Unified Data Platform. You will drive execution, ensure engineering excellence, and contribute to the evolution of a cloud-native, AI-ready data ecosystem serving multiple countries.
Key Responsibilities
- Lead and grow a team of engineers to deliver scalable, reliable data platform capabilities
- Own end-to-end delivery of platform components (design → build → run) with clear accountability for quality, performance, and reliability
- Partner with Product Managers to translate business needs into scalable technical solutions
- Contribute to and implement target data architecture aligned with enterprise standards
- Drive best practices in data engineering, DevOps, and platform reliability
- Ensure systems are built with observability, cost efficiency, and scalability in mind
- Collaborate with domain teams and region specific (LOB) teams to enable reusable data products and standardized platform capabilities
- Identify and resolve systemic issues, focusing on long-term engineering health
- Evaluate new technologies and lead proof-of-concepts where required
- Build strong engineering culture with focus on ownership, accountability, and continuous improvement
Data & Technical Expertise
- 14+ years of overall experience with 8+ years in data engineering, data platforms, or distributed data systems
- Strong hands-on experience with Google Cloud Platform (GCP), especially:
- BigQuery (data warehousing, performance tuning, cost optimization, partitioning/clustering)
- Dataflow (Apache Beam) for large-scale batch and streaming pipelines
- Pub/Sub for real-time ingestion and event-driven architectures
- Cloud Composer (Airflow) for orchestration and workflow management
- GCS (Cloud Storage) as part of lakehouse or staging architectures
- Deep understanding of modern data architectures including Lakehouse patterns on GCP and distributed processing systems
- Strong experience designing and operating end-to-end data pipelines (ingestion to transformation to serving) at scale
- Expertise in real-time and streaming architectures, including event design, schema evolution, and fault-tolerant processing
- Hands-on programming skills in Python, Spark (Scala), with experience in building distributed data applications
- Experience implementing CI/CD for data pipelines, including versioning, testing, and deployment automation
- Strong understanding of data modelling and optimization for analytical workloads in BigQuery
- Practical exposure to data product thinking, including:
- Data contracts and schema governance
- Discoverability and reuse across domains
- Ownership and lifecycle management
- Familiarity with MLOps and AI-enabled data platforms on GCP, including support for:
- Feature engineering pipelines
- Model training and inference workflows
- Integration with Vertex AI (preferred)
- Strong focus on platform reliability and observability, including monitoring, alerting, lineage, and data quality frameworks
- Experience managing cost-performance trade-offs in GCP (e.g., BigQuery cost controls, Dataflow optimization)
- Proven ability to work in globally distributed, federated environments, enabling standardization across multiple teams and geographies
- Awareness of evolving trends in cloud-native data platforms, data mesh, and event-driven architectures, with the ability to apply them pragmatically
Leadership & Collaboration
- Proven ability to lead and grow high-performing engineering teams with a strong focus on ownership, accountability, and continuous improvement
- Ability to operate effectively in a federated, multi-country environment, influencing teams without direct authority
- Strong stakeholder management skills, with experience collaborating across product, architecture, and business teams
- Clear and concise communication skills, with the ability to articulate complex technical concepts to senior leadership
- Experience fostering a product and platform mindset within engineering teams
