Description

Key Responsibilities:

 Develop ETL/ELT processes to extract data from various sources, transform it, and load it into BigQuery or other target systems. Build and maintain data models, data warehouses, and data lakes for analytics and reporting. Design and implement scalable, secure, and efficient data pipelines on Google Cloud Platform using tools such as Dataflow, Pub/Sub, cloud run, Python and linux scripting. Optimize BigQuery queries, manage partitioning and clustering, and handle cost optimization. Integrate data from on-premise and cloud systems using Cloud Storage, and APIs. Work closely with DevOps teams to automate deployments using Terraform, Cloud Build, or CI/CD pipelines. Ensure security and compliance by applying IAM roles, encryption, and network controls. Collaborate with data analysts, data scientists, and application teams to deliver high-quality data solutions. Implement best practices for data quality, monitoring, and governance. Required Skills and Experience: Bachelor s degree in Computer Science, Information Technology, or related field. Minimum 8 years of experience in data engineering, preferably in a cloud environment. Minimum 3 years of hands-on and strong expertise in Google Cloud Platform services: o BigQuery, Cloud storage, Cloud run, Dataflow, Cloud SQL, AlloyDB, Cloud Balancer, PubSub, IAM, Logging and Monitoring. Proficiency in SQL, Python and Linux scripting. Prior experience with ETL tools such as Datastage, Informatica, SSIS Familiarity with data modeling (star/snowflake) and data warehouse concepts. Understanding of CI/CD, version control (Git), and Infrastructure as Code (Terraform). Strong problem-solving and analytical mindset. Effective communication and collaboration skills. Ability to work in an agile and fast-paced environment. Google Cloud Platform Professional Data Engineer or Cloud Architect certification is a plus

Key Skills

Education

Bachelor's degree