Director of Data Engineering

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.
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 a focus on ownership, accountability, and continuous improvement.
Requirements:
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; and 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 and 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 modeling 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 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.

Experience
14-18 yrs