GCP Data Engineer - Customer Value and Digital Engagement Department

Join the data transformation program at T‑Mobile to co-create a program changing daily data work. Work with cutting-edge technologies, influence data strategy, and develop future competencies in an innovation-friendly culture. Create space for experimentation, rapid prototyping, and solutions shaping the future of data-driven telecommunications.

• Minimum 2–3 years experience as Data Engineer (Senior: min. 5 years plus leadership experience).
• Practical experience with GCP, especially BigQuery (advanced SQL, optimization, cost management), Cloud Storage (data management, versioning), Pub/Sub, Dataflow (Apache Beam) or Dataproc (Spark), and Cloud Composer (Airflow) for pipeline orchestration.
• Basic knowledge of GCP security (IAM, KMS, DLP).
• Very good SQL skills (query optimization, aggregations, working with large datasets).
• Programming experience in Python (e.g., pandas, PySpark, automated tests).
• Ability to design data models (Kimball, Data Vault, Dimensional Modeling) and build data layers (raw/bronze, curated/silver, semantic/gold).
• Experience with automation tools: Terraform (IaC) and CI/CD (e.g., GitLab CI, Cloud Build).
• Experience in data quality assurance (testing, monitoring, alerting).
• Ability to work with monitoring and diagnostic tools (Cloud Monitoring, Logging).
• Experience creating technical documentation and participating in code reviews.

Nice to have:

• Google certifications (e.g., Google Professional Data Engineer, Associate Cloud Engineer).
• Experience with data management tools such as dbt, Dataform (data transformation and documentation).
• Experience with Dataplex, Data Catalog (data governance).
• Teamwork and communication skills with business, technical, and analytical teams.
• Proactivity, responsibility, and quality focus in data area.
• English language proficiency at minimum B2 level.

• Design, develop, and maintain data pipelines (ETL/ELT) in GCP environment.
• Integrate data from various sources (APIs, databases, files) into BigQuery and Cloud Storage.
• Design and optimize data models in data warehouses (Kimball, Data Vault, Dimensional Modeling).
• Implement streaming solutions (e.g., Pub/Sub, Dataflow, Apache Beam).
• Automate data processes using Cloud Composer (Airflow).
• Ensure data performance, quality, security, and scalability.
• Collaborate with data analytics, Data Science, and business teams.
• Create technical documentation and participate in code reviews.
• Monitor and resolve performance issues and optimize cloud costs.
• Optimize performance and costs of implemented solutions.

For Senior:

• Lead a micro-team (2–4 people), plan work/sprints, prioritize backlog.
• Facilitate business workshops, translate requirements into solutions and SLA/SLO.