Gcp Data Engineer

Role & responsibilities

• Strong hands-on GCP Data Engineering experience across scalable cloud data platforms.
• Expertise in Google Cloud Storage, BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer, Cloud Functions, Cloud Monitoring, IAM and Secret Manager.
• Strong experience in batch and real-time streaming using Pub/Sub, Dataflow / Apache Beam, Spark Structured Streaming or Kafka.
• Hands-on coding proficiency in Python, PySpark and SQL, including debugging and performance optimization.
• Experience building high-volume, low-latency ingestion and transformation pipelines with retry, idempotency, deduplication and dead-letter handling.
• Strong experience in Databricks, Delta Lake, Structured Streaming, Auto Loader and Spark performance tuning.
• Experience handling TB/PB-scale datasets, partitioning, clustering, file compaction, schema evolution and data lifecycle management.
• Strong understanding of Data Lake, Lakehouse and modern cloud data-platform architectures.
• Practical data-modeling experience for analytics, reporting and downstream consumption.
• Experience implementing CI/CD using Cloud Build, GitHub Actions, Jenkins or equivalent, with Terraform for Infrastructure as Code.
• Strong troubleshooting, observability, production support and cloud cost-optimization skills.
• Excellent client communication, stakeholder management and problem-solving skills.

Preferred candidate profile

Design and develop scalable GCP Data Engineering solutions and cloud-native data platforms.

• Build batch and real-time pipelines using BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, Python, PySpark and Databricks.
• Develop and optimize Databricks, Delta Lake, Spark and Structured Streaming workloads for large-scale processing.
• Implement high-throughput streaming solutions using Pub/Sub and Dataflow, including windowing, watermarking, late-arriving data, duplicate handling and dead-letter queues.
• Handle high-volume TB/PB-scale data with focus on performance, scalability, reliability and cost optimization.

Develop complex SQL transformations, data models and analytics-ready BigQuery datasets using partitioning and clustering.

• Orchestrate pipelines using Cloud Composer / Airflow and implement CI/CD and Terraform-based Infrastructure as Code.
• Ensure data quality, governance, security, monitoring, alerting and production support.
• Collaborate with architects, stakeholders and cross-functional teams in an Agile delivery model.
• Troubleshoot and optimize end-to-end GCP, Databricks and streaming data pipelines.