Deutsche Telekom Digital Labs
Data Architect - GCP
- Compensation
- Not disclosed
- Location
- Gurgaon
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Architecture
- Posted
Skills
Must have
- 10+ years of experience in data architecture, data engineering, or cloud data platform development.
- Strong hands-on experience with Google Cloud Platform (GCP).
- Expertise in BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, BigLake, and Cloud Composer.
- Strong understanding of data lake, data warehouse, and lakehouse architectures.
- Experience with data modelling methodologies, including relational, dimensional (Kimball), and data vault.
- Hands-on experience building large-scale ETL/ELT pipelines.
- Strong SQL expertise and programming experience in Python.
- Experience with distributed data processing frameworks such as Apache Spark/PySpark.
- Good understanding of streaming architectures and event-driven data processing.
- Experience with Infrastructure as Code (Terraform preferred).
- Strong understanding of data governance, security, and metadata management.
- Excellent communication and stakeholder management skills.
Nice to have
- Experience working in agile product organisations.
- Exposure to Vertex AI or cloud-based machine learning services.
- Familiarity with modern data catalogue and governance tools such as Collibra, DataHub, and Alation.
- Experience with BI tools such as Looker, Tableau, or Power BI.
- Knowledge of Data Mesh principles and domain-driven data architecture.
- Exposure to modern AI/ML-enabled data platforms is an added advantage.
Full description
We are looking for an experienced GCP Data Architect to lead the design and implementation of enterprise-scale data platforms on Google Cloud Platform (GCP)This is a strategic role responsible for defining the organisation's data architecture, enabling modern analytics, and building scalable, secure, and high-performance data solutions. The ideal candidate will have strong expertise in GCP data services, data architecture, data engineering, cloud-native technologies, and enterprise data governance. You will work closely with product, engineering, analytics, and business teams to build reliable data platforms that support reporting, analytics, and future AI/ML initiatives.
The core responsibilities for the job include the following:
Data Architecture:
- Design and maintain enterprise-wide data architecture, including conceptual, logical, and physical data models.
- Define scalable data lake, data warehouse, and lakehouse architectures on Google Cloud Platform.
- Establish enterprise data standards, naming conventions, and architecture best practices.
- Evaluate and recommend appropriate data storage and processing patterns for structured and semi-structured data.
- Drive architecture decisions for new business initiatives and digital transformation programmes.
Google Cloud Platform (GCP):
- Design and implement cloud-native data solutions using GCP services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, BigLake, Cloud Composer, Cloud Run, and GKE.
- Optimise data platforms for scalability, performance, reliability, and cost efficiency.
- Ensure secure and compliant cloud architectures by implementing IAM, encryption, and governance best practices.
- Collaborate with cloud engineering teams on Infrastructure as Code (Terraform preferred) and CI/CD automation.
Data Engineering and Integration:
- Design and oversee enterprise ETL/ELT pipelines for batch and real-time data processing.
- Build scalable integration frameworks across multiple enterprise applications and data sources.
- Architect streaming data solutions using technologies such as Pub/Sub, Kafka, Spark, or Dataflow.
- Ensure end-to-end data lineage, monitoring, observability, and operational excellence.
- Drive modernisation of legacy data platforms to cloud-native architectures.
Analytics and Data Enablement:
- Design semantic and analytical data models for enterprise reporting and business intelligence.
- Partner with analytics teams to develop trusted, reusable, and governed data products.
- Enable self-service analytics by delivering well-documented, high-quality datasets.
- Support data platforms that can serve advanced analytics and future machine learning initiatives.