Senior Data Engineer

Role overview

Data platform architecture and delivery

• Design, develop, and maintain cloud-native GCP platforms, including enterprise data lakes, warehouses, marts, and analytics platforms.
• Build reusable, metadata-driven ETL/ELT frameworks and high-volume data pipelines for analytics and reporting.
• Implement event-driven and batch architectures and collaborate on enterprise data models, integration patterns, and platform strategy.
• Optimize BigQuery workloads and cloud infrastructure costs; evaluate AI/ML and Generative AI technologies for potential adoption.

Technical leadership and operations

• Mentor data engineers and ETL developers; establish coding standards, design patterns, engineering practices, CI/CD frameworks, and governance processes.
• Lead architecture reviews, code reviews, and technical design sessions; identify and mitigate delivery risks.
• Ensure data quality, testing, observability, monitoring, lineage, governance, security, privacy, and compliance.
• Support production through monitoring, troubleshooting, and root-cause analysis.

Experience and education

• Bachelor's or master's degree in Computer Science, Information Systems, Engineering, or a related field.
• 8+ years of experience in data engineering, ETL/ELT development, and data platform delivery.
• 5+ years of hands-on GCP experience and extensive experience building enterprise-scale data warehouses, data lakes, and analytics platforms.
• Expert SQL skills, including performance tuning, strong Python skills, and experience with modern data engineering frameworks.

Technical qualifications

• GCP services include BigQuery, Google Cloud Storage, Dataflow, Dataproc, Cloud Run, Pub/Sub, Cloud Composer/Apache Airflow, and Cloud Functions.
• Experience building CI/CD pipelines with GitHub, Terraform, Tekton, and infrastructure-as-code practices is required.
• Knowledge of distributed processing, cloud-native architecture, data quality, testing, observability, monitoring, governance, security, privacy, and compliance is required.
• Agile, Scrum, Lean, stakeholder management, and technical discussion skills are required.

Nice-to-have qualifications

• Google Cloud Professional Data Engineer and Associate Cloud Engineer certifications are listed as nice-to-have.
• Qlik Sense, Looker, Power BI, Snowflake, Databricks, AI, machine learning, Generative AI, LLM, and onshore/offshore team leadership experience are listed as nice-to-have.

Benefits

• Benefits include medical insurance, PF, gratuity, paid holidays, and other benefits.