Assistant Manager - Data Engineering

Job Summary


Key Responsibilities

• Maintain, troubleshoot, and optimize enterprise ETL/ELT and data integration pipelines connected to Snowflake.
• Take ownership of end-to-end production support, break/fix, and pipeline maintenance .
• Identify and resolve data anomalies, schema changes, ingestion issues, and integration failures.
• Perform data validation, manual QA, testing, and data cleanup across development and production environments.
• Maintain and enhance Snowflake data models and ensure data quality and consistency.
• Manage engineering tickets, enhancements, and defects using Jira and vendor portals.
• Follow GitHub branching, version control, and CI/CD best practices.
• Provide rapid support to business users through Microsoft Teams/Slack channels.
• Act as a key liaison between data engineers, data modelers, technical administrators, business users, and external vendors.
• Identify opportunities to improve pipeline reliability, support processes, and overall data integration efficiency.

Required Skills

• Strong experience in Data Integration, ETL/ELT, and enterprise data pipelines .
• Advanced SQL and Snowflake expertise, including data modeling, data shares, and semi-structured data.
• Strong knowledge of data modeling, data validation, QA, and data cleansing .
• Hands-on experience with GitHub , including branching, merging, and code management.
• Experience with Jira and Agile project workflows.
• Strong production support and incident management experience in SLA-driven environments.
• Experience troubleshooting data pipelines and resolving integration issues.
• Excellent communication and stakeholder-management skills, with the ability to translate technical issues for business and technical audiences.

Why Join Us?

• Own and lead a critical data platform: Take end-to-end ownership of enterprise data integration and Snowflake operations.
• Work across teams: Collaborate with engineering, business, technical administration, and external vendor teams.
• Drive continuous improvement: Shape processes that improve data quality, pipeline stability, and operational efficiency.
• Enable AI & advanced analytics: Your work will directly help internal teams transition from operational maintenance toward high-value AI and advanced analytics initiatives .

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