Job
Description
Key Responsibilities: Design and develop scalable data engineering solutions using Google Cloud Platform (GCP) and PySpark. Optimize Spark jobs for performance, scalability, and efficient resource utilization. Develop, maintain, and enhance ETL pipelines using BigQuery, Apache Airflow, and Cloud Composer. Collaborate with data scientists, analysts, and DevOps teams to translate business requirements into technical solutions. Ensure data integrity and security by implementing data governance, compliance, and security best practices. Monitor production workloads, troubleshoot performance issues, and implement enhancements. Implement and enforce coding standards, best practices, and performance tuning strategies. Support migration activities from on-premises data warehouses to GCP-based solutions. Mentor junior developers and contribute to knowledge-sharing within the team. Stay up to date with emerging cloud technologies, tools, and best practices in the data engineering ecosystem. Required Skills Experience: + years of total experience in data engineering. + years of hands-on experience with Google Cloud Platform (GCP), including BigQuery, Apache Airflow, and Cloud Composer. Strong expertise in developing and optimizing large-scale data processing solutions using Pyspark and Python. In-depth knowledge of SQL for data transformation and performance optimization. Proficiency in big data technologies such as Hadoop, HDFS, Hive, and YARN. Experience with distributed computing principles, data partitioning, and fault tolerance. Hands-on experience with CI/CD pipelines, version control (Git), and automation tools. Strong problem-solving, analytical, and troubleshooting skills. Experience working in Agile/Scrum environments. Excellent communication and collaboration skills to work with offshore and onshore teams. Skills: Analytical Thinking Google Cloud Platform Hadoop Hive Python SQL
