Senior Data Engineer Snowflake Cortex AWS Glue

Position: Lead Data Engineer (Snowflake)

Purpose of the Position:

To lead the data engineering team in architecting and delivering enterprise-scale Snowflake solutions for retail analytics, ensuring alignment with strategic business goals and modernization initiatives. he role will also drive Snowflake Cortex AI development, building AI agents and GenAI use cases that turn governed retail data into conversational, insight-driven experiences for business users.

Location: Pune/ Bangalore/Chennai/ Nagpur

Key Result Areas and Activities:

• Data Pipeline Development: Drive ingestion and transformation of multi-source retail data (MMS, CRM, Workday, Courion etc).
• Architecture Leadership: Define and implement Snowflake architecture for multi-layered retail data processing.
• ETL Strategy: Lead migration strategy from Talend to AWS Glue, ensuring automation and scalability.
• ETL Development: Build and optimize ETL workflows using Talend, AWS Glue and SQL stored procedures.
• Strategic Alignment: Align data engineering practices with retail business priorities (sales, customer, workforce, inventory)
• Data Modelling: Create and maintain Snowflake tables and layered views for business consumption.
• Cortex Agent Creation: Build, configure and deploy Cortex Agents that use Cortex Analyst (semantic models/views) for structured data and Cortex Search for unstructured content, with Streamlit in Snowflake as the user-facing conversational interface.
• Cortex Use-Case Delivery: Identify, prototype, and take Cortex use cases from POC to production (e.g., natural-language sales/inventory insights, product review and customer feedback analysis, document intelligence, demand forecasting).
• AI Readiness & Semantic Layer: Prepare AI-ready data foundations, including semantic models, curated views and metadata, so Cortex-based solutions give accurate, trusted responses.
• Data Governance: Implement standards for lineage, documentation, and compliance.
• Collaboration: Coordinate with team members, work closely with data analysts, business stakeholders, and regulatory teams to understand data needs and deliver solutions.
• Performance Optimization: Ensure efficient query performance, storage management and cost-effective data processing in Snowflake.
• Documentation & Governance: Maintain clear documentation of data models, transformations, and lineage to support audit and compliance requirements.
• Team Leadership: Mentor and manage engineers across different levels, ensuring delivery excellence.
• Innovation: Explore snowflake features like dynamic tables, automation, and advanced Snowflake features for efficiency.