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.
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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
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Partner with Product Managers to translate business needs into scalable technical solutions
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Contribute to and implement target data architecture aligned with enterprise standards
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Drive best practices in data engineering, DevOps, and platform reliability
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Ensure systems are built with observability, cost efficiency, and scalability in mind
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Collaborate with domain teams and region specific (LOB) teams to enable reusable data products and standardized platform capabilities
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Identify and resolve systemic issues, focusing on long-term engineering health
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Evaluate new technologies and lead proof-of-concepts where required
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Build strong engineering culture with focus on ownership, accountability, and continuous improvement
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10 - 14 years of overall experience with 6 to 8+ years in data engineering, data platforms, or distributed data systems
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BigQuery (data warehousing, performance tuning, cost optimization, partitioning/clustering)
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Dataflow (Apache Beam) for large-scale batch and streaming pipelines
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Pub/Sub for real-time ingestion and event-driven architectures
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Cloud Composer (Airflow) for orchestration and workflow management
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GCS (Cloud Storage) as part of lakehouse or staging architectures
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Deep understanding of modern data architectures including Lakehouse patterns on GCP and distributed processing systems
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Strong experience designing and operating end-to-end data pipelines (ingestion to transformation to serving) at scale
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Expertise in real-time and streaming architectures, including event design, schema evolution, and fault-tolerant processing
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Hands-on programming skills in Python, Spark (Scala), with experience in building distributed data applications
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Experience implementing CI/CD for data pipelines, including versioning, testing, and deployment automation
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Strong understanding of data modelling and optimization for analytical workloads in BigQuery
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Data contracts and schema governance
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Discoverability and reuse across domains
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Ownership and lifecycle management
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Feature engineering pipelines
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Model training and inference workflows
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Integration with Vertex AI (preferred)
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Strong focus on platform reliability and observability, including monitoring, alerting, lineage, and data quality frameworks
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Experience managing cost-performance trade-offs in GCP (e.g., BigQuery cost controls, Dataflow optimization)
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Proven ability to work in globally distributed, federated environments, enabling standardization across multiple teams and geographies
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Awareness of evolving trends in cloud-native data platforms, data mesh, and event-driven architectures, with the ability to apply them pragmatically
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Proven ability to lead and grow high-performing engineering teams with a strong focus on ownership, accountability, and continuous improvement
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Ability to operate effectively in a federated, multi-country environment, influencing teams without direct authority
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Strong stakeholder management skills, with experience collaborating across product, architecture, and business teams
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Clear and concise communication skills, with the ability to articulate complex technical concepts to senior leadership
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Experience fostering a product and platform mindset within engineering teams
