Key Responsibilities GCP Data Engineer Data Pipeline Development: Build and maintain scalable ETL/ELT pipelines using Apache Beam, Dataflow, Cloud Data Fusion, or Composer (Airflow) . Data Warehousing: Design and optimize data models, schemas, and storage solutions in BigQuery . Data Integration: Work with structured and unstructured data from multiple sources using Pub/Sub, Cloud Storage, and Kafka . Data Governance & Security: Implement best practices for data quality, lineage, and access control following industry standards. Automation & Performance Optimization: Use Terraform for IaC, CI/CD pipelines, and monitoring tools like Stackdriver for performance tuning. Collaboration: Work with Data Scientists, Analysts, and DevOps teams to streamline workflows and ensure efficient data accessibility . Must-Have Skills: Google Cloud Services: BigQuery, Dataflow, Cloud Composer (Airflow), Cloud Storage, Cloud Functions, Pub/Sub Programming: Strong proficiency in Python, SQL (advanced queries, stored procedures, indexing, partitioning) ETL & Data Processing: Experience with Apache Beam, Spark, DataProc Infrastructure as Code: Terraform, Cloud Deployment Manager Data Security & Governance: IAM, VPC, encryption, GDPR compliance Nice-to-Have Skills: Experience with Kafka, Snowflake, Databricks Machine Learning Pipelines (AI/ML on GCP) Experience with Streaming Data Processing (Flink, Kinesis, Pub/Sub) DevOps/CI-CD (Jenkins, GitHub Actions, Cloud Build) Qualifications: Bachelor’s or Master’s in Computer Science, Data Engineering, or related field 3-8 years of experience in Data Engineering & GCP ecosystem GCP Certifications (Professional Data Engineer preferred)