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.
- Solid 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.

📌 Gcp Data Engineer (Bengaluru)
🏢 Infosys
📍 Bengaluru