• GCP BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, batch and real-time streaming, Databricks , Pyspark 2. Python, solid SQL Databricks with Delta Lake and Structured Streaming Responsibilities and JD in brief along with additional criteria to be considered:
• 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.
Responsibilities and JD in brief along with additional criteria to be considered:
• 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-lette
📌 GCP Data Engineer (Noida)
🏢 Infosys
📍 Noida