IN_Manager_ Data Engineer GCP_Data and Analytics_Advisory

Job Description Summary

We are seeking an experienced Data Engineer - GCP to design, build, and operate scalable, reliable, and cost-efficient data pipelines on Google Cloud Platform. The role involves hands-on development using BigQuery, Dataflow, Dataproc, Cloud Composer, Pub/Sub, Cloud Storage, Python, SQL, and related GCP-native services, along with close collaboration with analytics, AI/ML, architecture, and business teams.

Responsibilities

• Design, build, and maintain end-to-end batch and streaming data pipelines on Google Cloud Platform
• Develop scalable data processing solutions using BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage
• Build and optimize Cloud Composer / Airflow DAGs for orchestration, scheduling, retries, and dependency management
• Ingest, transform, and curate large-scale structured, semi-structured, and streaming datasets
• Develop reusable ETL/ELT frameworks using Python, SQL, Apache Beam, and Spark / PySpark where applicable
• Implement BigQuery performance and cost optimization through partitioning, clustering, query tuning, and data model improvements
• Ensure data quality, reliability, lineage, observability, governance, and operational readiness
• Collaborate with data scientists, analysts, architects, and business teams to support analytics, reporting, and AI/ML workloads
• Implement secure access patterns using IAM, service accounts, encryption, and enterprise compliance standards
• Provide production support, troubleshoot pipeline failures, and resolve performance bottlenecks
• Document technical designs, data flows, operational procedures, and runbooks
• Mentor junior engineers and contribute to engineering best practices for GCP data platforms

Mandatory skill sets

• 4+ years of experience as a Data Engineer with strong hands-on Google Cloud Platform expertise
• Strong experience with BigQuery, GoogleSQL / SQL, and data warehousing concepts
• Hands-on experience with Dataflow and Apache Beam for batch and streaming pipelines
• Experience with Dataproc, Spark, or PySpark for distributed data processing
• Experience with Cloud Composer / Airflow for orchestration and workflow management
• Experience with Pub/Sub and Cloud Storage for ingestion and data lake patterns
• Strong Python skills for data engineering, automation, and reusable pipeline development

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