Description

Define and own the end-to-end data architecture on Google Cloud Platform, ensuring modernization from legacy batch systems to unified batch-and-streaming patterns.

Responsibilities

• Architect data foundations supporting Vertex AI and Generative AI, implementing Lakehouse patterns with BigQuery and BigLake.
• Design and orchestrate ETL/ELT pipelines using Cloud Composer (Airflow) and Dataflow (Apache Beam).
• Implement governance as code, automating data quality, lineage, and cataloging via Dataplex and Data Catalog.
• Establish Secure-by-Design principles, managing IAM, encryption, and PII redaction using DLP API for global compliance.
• Optimize GCP costs through FinOps and ensure performance tuning for BigQuery, AlloyDB, and Cloud Bigtable.

Required Skills

• 12+ years in Data Engineering/Architecture, including 3+ years specifically on Google Cloud Platform.
• Expert-level SQL and proficiency in Python for data processing and automation.
• Deep experience with BigQuery (Storage & ML), Cloud Storage, Pub/Sub, and AlloyDB.
• Proficiency in Terraform for Infrastructure-as-Code and GitHub Actions/Cloud Build for CI/CD.
• Hands-on experience with Dataplex, Data Catalog, and DLP API for data governance and security.
• Proven track record managing large-scale data migrations from on-prem to cloud or multi-cloud.
• Strong understanding of Data Mesh, Data Fabric, and FinOps methodologies.

Preferred Skills

• Google Professional Data Engineer or Cloud Architect certifications.
• Experience mentoring data engineering teams and communicating technical roadmaps to stakeholders.

Key Skills

Education

Bachelor's degree