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Parkar Global Technologies

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GCP Data Engineer - (Databricks)

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Description


Role Overview

We are looking for a Senior Data Engineer with strong hands-on experience in building and optimizing scalable data pipelines on Google Cloud Platform (GCP) and Databricks. This role requires deep technical expertise in data engineering, pipeline development, and cloud-native solutions, with the ability to work closely with cross-functional teams to deliver high-quality data systems.


Key Responsibilities

  • Design, build, and optimize scalable batch and real-time data pipelines on GCP and Databricks.
  • Develop and maintain ETL/ELT workflows for diverse data sources.
  • Build robust data ingestion frameworks and support data migration initiatives.
  • Develop and optimize Spark/PySpark workloads in Databricks, including notebooks, jobs, and Delta Lake tables.
  • Manage workflow orchestration, scheduling, and pipeline dependencies (Cloud Composer, Databricks Workflows).
  • Ensure efficient and reliable data processing across environments.
  • Optimize query performance and data processing jobs, including Databricks cluster and job tuning.
  • Implement data validation, quality, and reconciliation frameworks.
  • Monitor pipelines to ensure availability, reliability, and performance.
  • Collaborate with cross-functional teams to translate business requirements into solutions.
  • Contribute to data architecture and design discussions.
  • Follow best practices for version control, CI/CD, and deployments.
  • Ensure compliance with security, governance, and data standards, including access control and cataloging in Databricks.
  • Support production systems through troubleshooting and continuous improvement.

Must-Have Skills

  • 6-8 years of experience in data engineering.
  • Strong hands-on experience with GCP (BigQuery, Dataflow, Pub/Sub, Cloud Storage).
  • Hands-on experience with Databricks, including Delta Lake, Databricks Workflows/Jobs, and notebook-based development.
  • Experience with Spark /PySpark and SQL optimization.
  • Hands-on with Cloud Composer (Airflow).
  • Strong understanding of data modeling and pipeline design.
  • Experience with batch + streaming data processing.
  • Familiarity with CI/CD and Git-based workflows.
  • Strong problem-solving and analytical skills.

Skills

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