Snowflake(Python, Reporting)

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.