About The Opportunity
A fast-scaling technology solutions provider in the Cloud Data Engineering and AI/ML enablement space, we architect and deploy scalable, secure, and cost-optimized data platforms on Google Cloud Platform (GCP) for enterprise clients across BFSI, Healthcare, and Retail. Our data engineering teams build end-to-end data pipelines, modern data lakes, and real-time analytics solutions that power business intelligence, ML models, and generative AI applications—delivering measurable ROI through clean, governed, and production-ready data infrastructure.
Role & Responsibilities
- Design, build, and optimize scalable data pipelines on GCP using Dataflow, Pub/Sub, BigQuery, and Cloud Storage.
- Develop and maintain ETL/ELT workflows using Python, SQL, and GCP-native tools for batch and streaming data ingestion.
- Implement data governance, lineage, and quality monitoring using Dataplex, Data Catalog, and custom validation frameworks.
- Collaborate with Data Scientists and ML Engineers to productionize feature stores and model training pipelines using Vertex AI and BigQuery ML.
- Automate deployment and infrastructure provisioning via Terraform, Cloud Build, and CI/CD pipelines.
- Monitor system performance, cost, and scalability—optimizing queries, partitioning strategies, and resource allocation across GCP services.
Skills & Qualifications
- Must-Have
- BigQuery
- Dataflow
- Pub/Sub
- Terraform
- Python
- SQL
- Cloud Storage
- Vertex AI
- Preferred
- Dataplex
- Looker
- Apache Beam
Benefits & Culture Highlights
- Work on enterprise-grade GCP projects with global clients across high-impact industries.
- Access to GCP certification sponsorships and hands-on upskilling in modern data stack technologies.
- On-site collaborative environment with agile teams, innovation sprints, and direct mentorship from cloud architects.
Skills: cloud,gcp,ml,infrastructure
