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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