Senior Data Engineer

Mphasis is a company that specializes in providing technology and business solutions. They are seeking a Senior Data Engineer to architect and manage scalable, secure, cloud-native data platforms primarily on Google Cloud Platform, while optimizing data pipelines and enabling AI/ML integrations.

Responsibilities:

• Architect and own scalable, secure, cloud-native data platforms on Google Cloud Platform
• Design, build, and optimize batch and real-time data pipelines using BigQuery, Dataflow, Pub/Sub, and Dataproc
• Lead BigQuery performance tuning and cost optimization (partitioning, clustering, query efficiency)
• Orchestrate workflows using Cloud Composer (Apache Airflow)
• Enable Al/ML and GenAl integration via Vertex Al and BigQuery ML
• Enforce data governance, security, reliability, and FinOps best practices
• Mentor engineers, conduct design/code reviews, and set enterprise data engineering standards
• Collaborate with product, analytics, and data science teams to deliver business-critical insights

Requirements:

• 5 - 8 Years of experience
• Architect and own scalable, secure, cloud-native data platforms on Google Cloud Platform
• Design, build, and optimize batch and real-time data pipelines using BigQuery, Dataflow, Pub/Sub, and Dataproc
• Lead BigQuery performance tuning and cost optimization (partitioning, clustering, query efficiency)
• Orchestrate workflows using Cloud Composer (Apache Airflow)
• Enable Al/ML and GenAl integration via Vertex Al and BigQuery ML
• Enforce data governance, security, reliability, and FinOps best practices
• Mentor engineers, conduct design/code reviews, and set enterprise data engineering standards
• Collaborate with product, analytics, and data science teams to deliver business-critical insights
• GCP Data Services: BigQuery, Dataflow (Apache Beam), Pub/Sub, Cloud Storage, Cloud Composer, Dataproc
• Advanced SQL
• Python (Java/Scala a plus)
• ETL/ELT, streaming & batch processing, data modeling, distributed systems
• Lakehouse, Apache Iceberg, Data Mesh concepts
• Vertex Al, BigQuery ML, GenAl-ready pipelines
• Terraform, CI/CD, DataOps practices
• Architecture ownership, mentoring, stakeholder communication, problem solving
• Google Cloud Professional Data Engineer (strongly preferred / often mandatory)
• Looker
• GCP vertex
• Data virtualization (Trenio or equivalent)
• basic knowledge of network connectivity (knowledge on data catalog, DLP, BQDTS, STS and other data transfer methodologies)
• Reporting background (powerbi)
• ICEBERG