Data Manager

Objective of the Role:

As an Engineering Manager, 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.

You Will:

• 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

You Must Have:

• 10 - 14 years of overall experience with 6 to 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