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Tier-1 brand, metro location, and broad popular data engineering skillset increase competition.
Low: SQL, Python, and cloud data engineering skills are widely transferable across industries.
Moderate: required SQL/Python, cloud/DW familiarity and CI/CD exposure but no explicit years.
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Maintain data pipelines and ETL processes to support reporting, analytics, AI, and ML applications across business units.
Manage data ingestion, processing, storage in cloud data warehouse platforms like Snowflake, and develop data transformation workflows using tools such as DBT.
Support deployment and release management of data workflows, implement automated testing, data validation, and troubleshoot pipeline issues with senior engineers.
Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field, or equivalent practical experience.
Working knowledge of SQL and basic to intermediate Python for data processing.
Familiarity with cloud data warehouse platforms (Snowflake, Big Query, or similar), Git/version control, and CI/CD workflows.
Work Experience Required: Not explicitly mentioned in the JD.
Experience or strong interest in modern data engineering practices supporting AI/ML workflows and MLOps concepts.
Comfortable working collaboratively in a team environment supporting diverse data consumers across multiple regions.
Familiarity with event-driven streaming data systems, containerization (Docker), and data governance principles.