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Big Data Engineer

This role involves managing and optimizing Big Data environments (PySpark, Big Query, Airflow) across Google Cloud, AWS, or Azure platforms, ensuring efficient, secure, and cost-effective operations. Key responsibilities include 24x7 support, data pipeline optimization, automation, and troubleshooting, with a strong emphasis on Dev Ops, CI/CD, and disaster recovery.

Roles and Responsibilities:

• Participate in 24x7x365 rotational shift support and operations for SAP environments.
• Serve as a team lead responsible for maintaining the upstream Big Data ecosystem, handling millions of financial transactions daily using PySpark, Big Query, Dataproc, and Google Airflow.
• Streamline and optimize existing Big Data systems and pipelines while developing new ones, ensuring efficient and cost-effective performance.
• Manage the operations team during your designated shift and make necessary changes to the underlying infrastructure.
• Provide day-to-day support, improve platform functionality using Dev Ops practices, and collaborate with development teams to enhance database operations.
• Architect and optimize data warehouse solutions using Big Query to enable efficient data storage and retrieval.
• Install, build, patch, upgrade, and configure Big Data applications.
• Administer and configure Big Query environments, including datasets and tables.
• Ensure data integrity, availability, and security on the Big Query platform.
• Implement partitioning and clustering strategies for optimized query performance.
• Define and enforce access policies for Big Query datasets.
• Set up query usage caps and alerts to control costs and prevent overages.
• Troubleshoot issues in Linux-based systems with strong command-line proficiency.
• Create and maintain dashboards and reports to monitor key metrics such as cost and performance.
• Integrate Big Query with other GCP services like Dataflow, Pub/Sub, and Cloud Storage.
• Enable Big Query usage through tools such as Jupyter Notebook, Visual Studio Code, and CLI utilities.
• Implement data quality checks and validation processes to maintain data accuracy.
• Manage and monitor data pipelines using Airflow and CI/CD tools like Jenkins and Screwdriver.
• Collaborate with data analysts and scientists to gather data requirements and translate them into technical implementations.
• Provide guidance and support to application development teams for database design, deployment, and monitoring.
• Demonstrate proficiency in Unix/Linux fundamentals, scripting in Shell/Perl/Python, and using Ansible for automation.
• Contribute to disaster recovery planning and ensure high availability, including backup and restore operations.
• Experience with geo-redundant databases and Red Hat clustering is a plus.
• Ensure timely delivery within defined SLAs and project milestones, adhering to best practices for continuous improvement.
• Coordinate with support teams including DB, Google, PySpark data engineering, and infrastructure.
• Participate in Incident, Change, Release, and Problem Management processes.

Must Have Skills,

Experience:

• 4–8 years of relevant experience.
• Strong experience with Big Data technologies including PySpark, Big Query, and Google Airflow.
• Hands-on expertise in cloud platforms (Google Cloud, AWS, or Azure) and Linux system troubleshooting.
• Proficiency in automation and Dev Ops tools such as Shell/Python scripting, CI/CD processes, and Ansible.