Technology- Snowflake, Python, Reporting, Airflow, DBT, AWS, Azure, Data Modeling
Key Responsibilities:
- Lead end-to-end implementation of Snowflake-based data solutions, including architecture, development, and production rollout.
- Design and build scalable ELT/ETL pipelines using Python to ingest, transform, and validate data from multiple sources.
- Develop and optimize Snowflake objects (schemas, tables, views) and implement efficient data modeling patterns for analytics and reporting.
- Ensure performance tuning and cost optimization in Snowflake through clustering strategies, query optimization, and warehouse sizing best practices.
- Build and maintain robust data quality checks, reconciliation processes, and automated monitoring for pipeline reliability.
- Partner with reporting and analytics stakeholders to translate business requirements into curated datasets and reporting-ready layers.
- Establish coding standards, review pull requests, and mentor team members to improve engineering quality and delivery consistency.
- Drive secure data access patterns, role-based controls, and governance practices aligned with organizational needs.
- Collaborate with cross-functional teams to plan releases, manage dependencies, and ensure timely delivery across initiatives. Minimum Qualifications:
- BTECH, MTECH, MCA, or MSC in Computer Science, Information Technology, or a related field.
- 7–9 years of overall experience with strong hands-on expertise in Snowflake and Python for data engineering use cases.
- Proven experience designing and implementing data pipelines and transformation logic for analytics and reporting consumption.
- Strong SQL skills with experience in building performant queries and maintaining data models in Snowflake.
- Experience supporting reporting needs by delivering curated datasets, semantic-ready views, or reporting layers. Preferred Qualifications:
- Experience with Snowflake advanced capabilities such as Time Travel, Streams & Tasks, Secure Data Sharing, and data governance features.
- Strong Python engineering practices including modular design, testing, logging, and building reusable utilities for data processing.
- Experience designing reporting-friendly data models (star/snowflake schemas) and enabling self-service analytics through well-documented datasets.
- Exposure to orchestration and automation approaches to schedule, monitor, and recover pipelines reliably at scale.
- Demonstrated technical leadership through mentoring, solution design ownership, and driving best practices across teams.
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