Engineering Manager

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.

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 the 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 standardised platform capabilities.
• Identify and resolve systemic issues, focusing on long-term engineering health.
• Evaluate new technologies and lead proofs-of-concept where required.
• Build a strong engineering culture with a focus on ownership, accountability, and continuous improvement.

Requirements:

• 10 - 14 years of overall experience with 6 to 8+ years in data engineering, data platforms, or distributed data systems.
• BigQuery (data warehousing, performance tuning, cost optimisation, 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 optimisation for analytical workloads in BigQuery.
• Data contracts and schema governance.
• Discoverability and reuse across domains.
• Ownership and lifecycle management.
• 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 optimisation).
• Proven ability to work in globally distributed, federated environments, enabling standardisation 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 and 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.