We are seeking a seasoned GCP Data Warehouse Architect to lead the design, development, and governance of scalable, secure, and high-performance data architectures on Google Cloud Platform (GCP). The ideal candidate will have a proven track record in architecting enterprise-grade data warehouses and large-scale data pipelines that support real-time analytics, business intelligence, machine learning, and AI-driven decision-making across global organizations.
This role is pivotal in shaping the future of our data platform—enabling data democratization, accelerating time-to-insight, and ensuring compliance with data governance, security, and performance standards. You will work closely with data scientists, engineers, business stakeholders, and cloud architects to deliver robust, future-ready data solutions.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- 8+ years of experience in data engineering, with 5+ years focused on GCP and large-scale data warehouse implementations.
- Deep expertise in Google Cloud Platform (GCP) services :
- BigQuery (including federated queries, partitioning, clustering, materialized views, BI Engine)
- Cloud Storage (buckets, lifecycle policies, IAM)
- Dataflow (Apache Beam, streaming & batch)
- Cloud Composer (Airflow workflows)
- Pub / Sub, Dataproc, Dataplex, Data Catalog, Cloud Functions, Cloud SQL, Firestore
- Proven experience in designing and managing enterprise data warehouses (e.g., 100+ TB+ data volume, 1000+ daily jobs).
- Strong understanding of data modeling, ETL / ELT patterns, data quality, and metadata management.
- Experience with infrastructure-as-code (Terraform, Pulumi) and CI / CD pipelines (Cloud Build, GitHub Actions).
- Familiarity with data governance tools (e.g., Collibra, Informatica, Alation) and data observability platforms.
- Excellent communication skills with the ability to translate technical concepts for business stakeholders.
Preferred Qualifications
Google Cloud Professional Data Engineer or Google Cloud Professional Architect certification.Experience with multi-cloud or hybrid data architectures (e.g., integrating GCP with AWS / Azure).Hands-on experience in AI / ML pipeline integration with BigQuery ML, Vertex AI, or custom models.Knowledge of real-time analytics, stream processing, and event-driven architectures.Experience in regulated industries (e.g., finance, healthcare, energy, logistics) with strict compliance requirements.