Assistant Manager - Data Engineering

• Data Pipeline Development: Design, develop, and maintain scalable and fault-tolerant batch and real-time data pipelines using AWS and Snowflake. Build robust ETL/ELT workflows for processing structured and unstructured data.
• Data Warehousing Modeling: Design and optimize Snowflake data warehouses and implement data models using Dimensional Modeling, Star Schema, Snowflake Schema, and Data Vault methodologies. Leverage Snowflake capabilities such as Snowpipe, Streams, Tasks, Time Travel, clustering, and materialized views.
• AWS Cloud Engineering: Develop cloud-based data solutions using Amazon S3, AWS Glue, Lambda, EMR, Athena, Step Functions, EC2, and IAM, ensuring scalability, reliability, security, and operational efficiency.
• Performance Cost Optimization: Optimize Snowflake virtual warehouses, SQL queries, data processing workloads, and storage utilization. Implement query tuning and cloud cost-optimization strategies.
• Data Governance Security: Implement data quality, governance, security, and access-control best practices across AWS and Snowflake environments. Manage RBAC, permissions, data integrity, and compliance requirements.
• Stakeholder Collaboration: Work closely with Data Scientists, Data Analysts, Product Managers, and business stakeholders to understand requirements and deliver reliable, analytics-ready datasets and scalable data solutions.
• Technical Leadership Mentoring: Mentor junior data engineers, conduct code reviews, promote best practices in development and testing, and contribute to technical design decisions and continuous improvement initiatives.
• CI/CD Engineering Practices: Implement automated testing, version control, CI/CD practices, data quality checks, and reliable deployment processes using modern data engineering frameworks.

• AWS, Snowflake, SQL, Python, ETL/ELT, Data Warehousing, Data Modeling, Apache Airflow, AWS Glue, Snowpipe, Streams, Tasks, Data Quality, CI/CD, Git, and Cloud Data Engineering.
• 58 years of hands-on experience in Data Engineering, Data Warehousing, or Data Architecture.
• Strong expertise in Snowflake architecture, query optimization, virtual warehouses, resource monitors, clustering, materialized views, Snowpipe, Streams, and Tasks.
• Hands-on experience with AWS S3, EC2, Lambda, Glue, Athena, Step Functions, IAM, with exposure to EMR preferred.
• Advanced SQL skills including complex joins, CTEs, window functions, analytical functions, and query tuning.
• Strong Python programming skills for data processing, API integration, automation, and scripting.
• Good understanding of Kimball methodology, Dimensional Modeling, Data Vault, Star Schema, and Snowflake Schema.
• Experience with Apache Airflow, AWS Step Functions, or similar workflow orchestration tools.
• Knowledge of data quality frameworks, automated testing, CI/CD, Git, security, governance, and cloud cost optimization.

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