Deutsche Telekom Digital Labs
Website:
dtdl.in
Job details:
Platform Engineering | Google Cloud | Data & AI Enablement
We are looking for a Senior Developer to help build and evolve the CI/CD frameworks used by our Data and AI Platform teams.
This role is focused on building reusable platform capabilities rather than one-off project-specific implementations. You will work closely with data engineers, AI/ML engineers, and platform teams to design reusable pipelines, automation frameworks, deployment patterns, and self-service engineering capabilities for modern data and AI workloads on Google Cloud Platform.
The right person for this role is hands-on, comfortable writing production-quality code, and has a strong understanding of both cloud services and CI/CD engineering practices. You should be able to take complex platform requirements and turn them into scalable, reusable frameworks that engineering teams can easily adopt.
Key Responsibilities
Build Platform CI/CD Frameworks
- Design and develop reusable GitLab CI/CD frameworks for Data and AI platform teams.
- Create reusable pipeline templates, deployment workflows, validation steps, and automation standards that can be adopted across projects and environments.
- Build self-service deployment capabilities and golden-path engineering workflows for Data & AI teams.
Enable GCP Data Platform Deployments
- Build deployment patterns and automation for core GCP data services including:
- Dataproc
- BigQuery
- Dataform
- Cloud Composer
- Google Cloud Storage
- Dataflow
- Bigtable
- Cloud Spanner
- Enable teams to deploy, configure, and operate these services through reliable and repeatable CI/CD processes.
- Design frameworks supporting multi-environment and multi-tenant deployment patterns.
Support Data Pipeline Delivery
- Design CI/CD workflows for:
- Batch data pipelines
- Streaming workloads
- Transformation logic
- Orchestration workflows
- Environment-specific releases
- Work with engineering teams to improve how data pipelines are built, tested, promoted, and deployed across environments.
Develop Internal Tools and Automation
- Build platform tools, APIs, CLIs, scripts, and reusable libraries using Python and Go.
- Focus on reducing repetitive work, improving developer experience, and making platform capabilities easier for teams to adopt.
- Apply software engineering best practices including testing, modular design, versioning, and maintainability to platform tooling and frameworks.
Partner with Engineering Teams
- Work closely with data, AI, platform, and infrastructure teams to understand engineering challenges and convert them into scalable platform solutions.
- Help define practical standards for:
- Pipeline design
- Deployment automation
- Release management
- Operational readiness
- CI/CD governance
Must-Have Qualifications
- Strong experience designing and building CI/CD frameworks for platform or engineering teams.
- Expert-level knowledge of Google Cloud Platform and hands-on experience with GCP data services.
- Deep experience with:
- Dataproc
- BigQuery
- Dataform
- Cloud Composer
- GCS
- Dataflow
- Bigtable
- Spanner
- Strong experience with GitLab and GitLab CI/CD pipeline design.
- Experience designing CI/CD pipelines for data platforms, analytics workloads, or AI/ML platform teams.
- Strong understanding of data pipeline design including:
- Batch processing
- Streaming architectures
- Orchestration
- Transformation workflows
- Environment promotion
- Strong application development experience with Python.
- Hands-on development experience with Go.
- Ability to build reusable frameworks, deployment templates, automation utilities, and engineering standards.
- Experience working with cross-functional engineering teams in platform, DevOps, or cloud engineering environments.
Technical Skills
Cloud Platform
- Google Cloud Platform (GCP)
GCP Data Services
- Dataproc
- BigQuery
- Dataform
- Cloud Composer
- GCS
- Dataflow
- Bigtable
- Spanner
CI/CD & Automation
- GitLab
- GitLab CI/CD
- Pipeline templates
- Deployment automation
- Environment promotion workflows
Programming
Data Engineering
- Data pipeline design
- Batch processing
- Streaming workloads
- Orchestration frameworks
Platform Engineering
- Reusable tooling
- Automation frameworks
- Developer enablement
- Self-service platforms
DevOps Practices
- Version control
- Release workflows
- Automated validation
- CI/CD governance
Click on Apply to know more.
