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

Deutsche Telekom Digital Labs

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