GCP Bigquery

GCP Bigquery

Good to have skills: Cloud Composer, Dataflow, Pub/Sub, Looker, Terraform Preferred Qualifications: • Experience designing dimensional or curated data models (e.g., star/snowflake) for analytics consumption in BigQuery. • Familiarity with orchestration and scheduling approaches for ETL workflows and dependency management. • Proven ability to improve pipeline reliability and performance through monitoring, alerting, and operational best practices. • Experience with data governance practices including dataset organization, access management, and documentation standards. • Strong stakeholder collaboration skills to translate business requirements into scalable, maintainable data solutions. Key Responsibilities: • Design, develop, and maintain BigQuery datasets, tables, views, and SQL transformations aligned to analytics and reporting needs. • Build and support ETL/ELT pipelines leveraging GCS for ingestion and Dataproc for distributed processing where required. • Optimize BigQuery performance and cost through partitioning, clustering, query tuning, and efficient data modeling practices. • Implement data quality checks, validation routines, and reconciliation processes to ensure trustworthy outputs. • Collaborate with cross-functional teams to gather requirements, define data contracts, and deliver curated datasets. • Monitor pipeline health, troubleshoot failures, perform root-cause analysis, and drive timely resolution. • Maintain clear technical documentation for pipelines, datasets, transformations, and operational runbooks. • Follow secure data handling practices and support access controls and governance standards within GCP. Minimum Qualifications: • Education: BTECH, MTECH, MCA, MSC. • 3–5 years of experience in data engineering or analytics engineering roles with hands-on BigQuery usage. • Strong SQL skills with experience building transformations, aggregations, and analytical datasets in BigQuery. • Experience working with ETL/ELT concepts, batch processing, and data pipeline troubleshooting. • Working knowledge of GCS for data storage, ingestion patterns, and lifecycle handling. • Exposure to Dataproc for Spark/Hadoop-based processing and job execution in GCP environments.