Data Engineering: SQL (advanced), Python/PySpark, pipeline design, performance tuning, data quality controls
Data Warehousing: DWH design, dimensional modeling, distributed processing concepts,
BigQuery optimization Good-to-Have Skills Infrastructure as Code: Terraform or GCP Deployment Manager MLOps exposure: model lifecycle, CI/CD for ML, experiment tracking, deployment automation, monitoring (framework exposure) Domain Preference (Healthcare) Healthcare domain exposure is preferred;

Job Description: GCP Lead / Architect (Data Engineering)
Experience: 12+ years We are seeking a GCP Lead / Architect with a strong Data Engineering foundation to design and deliver secure, scalable, and cost-optimized data platforms on Google Cloud Platform (GCP).
The role requires hands-on expertise across IAM, VPC, GCS, BigQuery, Vertex AI, GKE, Compute Engine, GitHub Actions, and Dataproc, along with strong experience in data warehouse design, distributed systems, and DevOps concepts Key Responsibilities Architecture & Solution Design Lead end-to-end architecture for data platforms on GCP including networking, security, compute, storage, and analytics components.
Define high-level design (HLD) and low-level design (LLD), architecture standards, and reference patterns for ingestion, transformation, serving, and governance. Drive architecture decisions balancing performance, reliability, scalability, cost, and security; perform design reviews and technical audits.
Data Engineering & Warehousing Architect and guide implementation of robust pipelines for structured / semi-structured / unstructured data using scalable patterns (batch + streaming where applicable) Develop and maintain data models, ETL/ELT workflows, and batch/streaming pipelines.
Build and optimize BigQuery-centric data warehouse/lakehouse solutions, including dimensional modeling, partitioning/clustering, query tuning, and workload optimization.
Lead DWH design: data modeling (conceptual/logical/physical), SCD strategies, conformed dimensions, data quality rules, and lineage considerations.
GCP Platform Engineering (Hands-on) Implement and enforce security and access controls using IAM (least privilege), service accounts, and org/policy guardrails. Engineer and support workloads on GKE and Compute Engine, including configuration, scalability, observability, and operational readiness.
Use GCS for governed storage and lifecycle, and Dataproc for Spark/Hadoop-based processing DevOps / CI-CD / Automation Build, manage, and optimize data pipelines using GCP-native tools and services.
Develop CI/CD automations with Git / GitHub Actions. Establish DevOps best practices: Git branching strategies, environment promotion, artifact/version management, IaC standards, and rollback strategies.
AI/ML Enablement (as needed) Collaborate with ML/DS teams to operationalize ML services using Vertex AI (training/inference integration, data access, and platform readiness).
Support patterns for secure AI consumption and governance where required (e.g., explainability, privacy controls, audit readiness).
Must-Have Technical Skills Google Cloud Platform (hands-on): IAM, VPC, GCS, BigQuery, Vertex AI, GKE, Compute Engine, Dataproc
CI/CD & DevOps: GitHub Actions, Git workflows, pipeline automation, environment management
Data Engineering: SQL (advanced), Python/PySpark, pipeline design, performance tuning, data quality controls
Data Warehousing: DWH design, dimensional modeling, distributed processing concepts,
BigQuery optimization Good-to-Have Skills Infrastructure as Code: Terraform or GCP Deployment Manager MLOps exposure: model lifecycle, CI/CD for ML, experiment tracking, deployment automation, monitoring (framework exposure) Domain Preference (Healthcare) Healthcare domain exposure is preferred;
Medicare STAR ratings experience is a strong plus Qualifications Bachelor s degree in Computer Science / Engineering or equivalent experience. Cloud certification(s) in GCP (Professional Cloud Architect / Data Engineer) preferred.