GCP Data Engineer

Requirements -

Cloud & Data Engineering

Design, build, and optimize data pipelines on GCP using BigQuery, Dataflow, Dataproc,cloud funtions, cloud run and Cloud Storage.

Implement ETL/ELT workflows for batch and streaming data ingestion.

Develop scalable data lake and warehouse architectures following best practices.

Optimize query performance through partitioning, clustering, compression, and schema design.(Big query, Bigtable , spanner and fire store)

Terraform , DBT knowledge is required with hand on experience

Data Processing & Modeling

Build real‑time streaming pipelines using Pub/Sub + Dataflow (Apache Beam).

Create dimensional models, star/snowflake schemas, and semantic layers.

Write efficient SQL and Python for transformations and automation.

Looker knowledge (very important)

Orchestration & Automation

Automate workflows using Cloud Composer (Airflow).

Implement CI/CD pipelines for data deployments using Cloud Build and Terraform integrated in GitHub

Manage versioning, testing, and environment configuration.

Security & Governance

Apply IAM role-based access, service accounts, VPC‑SC, and encryption policies.

Implement data quality checks and governance using Dataplex and validation frameworks.

Ensure compliance with GDPR and internal data security standards.(Encryption and decryption concepts and use of CMEK)

Operations & Monitoring

Set up monitoring dashboards and alerts using Cloud Monitoring & Logging.

Optimize resource usage to ensure cost‑efficient data processing.

Troubleshoot pipeline failures, performance bottlenecks, and data integrity issues.

Collaboration & Stakeholder Interaction

Partner with data analysts, ML teams, and product owners to define data needs.

Translate business requirements into scalable cloud architecture.

Document data flows, standards, lineage, and best practices for cross‑team adoption.