- Design and implement scalable, secure, and high-performance data architectures using Snowflake and Databricks
- Develop and manage ELT/ETL workflows using dbt, ensuring modular, testable, and well-documented transformations
- Develop and maintain cloud-native data pipelines on AWS (e.g., S3, Glue, Lambda, Redshift, EMR)
- Architect and optimize data models for analytics, reporting, and machine learning use cases
- Build and manage data ingestion, transformation, and orchestration workflows
- Leverage Python for data engineering, automation, and advanced data processing
- Implement and manage Feature Store solutions for ML lifecycle management
- Utilize Snowflake Cortex for AI/ML-powered data applications and advanced analytics
- Collaborate with data scientists, analysts, and business stakeholders to translate requirements into technical solutions
- Establish data governance, data quality, and security best practices
- Optimize performance, cost, and scalability of data platforms
- Mentor and guide data engineers and other team members
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