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Technology- Snowflake, python, SQL,Data modeling, ETL/ELT best practices, Performance tuning, Data quality frameworks, CI/CD for data pipelines
Key Responsibilities: Technical Leadership & Delivery
Lead end-to-end implementation of Snowflake-based data platforms, from design to production rollout.
Own technical planning, estimation, and execution for data engineering initiatives aligned to business goals.
Mentor team members through code reviews, design reviews, and engineering best practices. Snowflake Development
Design and implement Snowflake schemas, warehouses, and data models optimized for analytics and reporting workloads.
Develop and tune Snowflake SQL for performance, cost efficiency, and scalability.
Implement secure data access patterns using roles, grants, and governance controls. Python Data Engineering
Build robust Python-based ingestion and transformation components for batch and incremental processing.
Create reusable utilities for validation, logging, error handling, and operational monitoring.
Ensure data quality through automated checks, reconciliation, and exception management. Operational Excellence
Establish standards for deployment, observability, and incident response for data pipelines and Snowflake workloads.
Troubleshoot production issues, perform root-cause analysis, and drive continuous improvements. Minimum Qualifications:
BTECH, MTECH, MCA, or MSC in Computer Science, Engineering, or a related field.
7–9 years of overall experience in data engineering / data platform development.
Strong hands-on expertise in Snowflake including performance tuning and workload optimization.
Strong hands-on expertise in Python for building data pipelines and automation.
Advanced SQL skills for complex transformations, optimization, and analytics-ready modeling. Preferred Qualifications:
Experience designing scalable data models (dimensional, denormalized, or domain-oriented) for analytics consumption.
Proven ability to lead technical discussions with stakeholders and translate requirements into implementable designs.
Experience building reusable Python modules and maintaining high code quality through testing and reviews.
Knowledge of data governance concepts such as access controls, auditing, and data lifecycle management within Snowflake.
Track record of improving pipeline reliability and performance through monitoring, optimization, and automation.
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