2+ years in professional experience.Experience in designing, building, and maintaining scalable data pipelines and ETL processes to support business analytics and reporting needs.Experience in SQL for querying and transforming large datasets, and optimizing query performance in relational databases.Experience in Python for building and automating data pipelines, ETL processes, and data integration workflows.Experience with big data frameworks such as Apache Spark or PySpark for distributed data processing.Experience with data modeling principles for building scalable and efficient data architectures (e.g., star schema, snowflake schema).Experience with Databricks for managing and processing large datasets, implementing Delta Lake, and leveraging its collaborative environment.Experience with Google Cloud Platform (GCP) services like BigQuery, Dataflow, Pub/Sub, and Cloud Storage for end-to-end data engineering solutions.Experience with version control systems such as Git and CI/CD pipelines for managing code and deploying workflows.Experience with data governance and security best practices, including access control, data masking, and compliance with industry standards.Experience with monitoring and logging tools like Datadog, Cloud Logging, or ELK Stack for maintaining pipeline reliability.Experience in developing business intelligence reports and dashboards via tools such as Tableau, PowerBI, Sigma etc.Experience in understanding business requirements and translating them into technical requirements.Experience in designing solutions for complex data problems.Experience in delivering against several initiatives simultaneously as a multiplier.Experience with writing unit and functional tests.