Snowflake(Python, Reporting)

Responsibilities :

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.

7â€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.

Additional Responsibilities:

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.

Technical and Professional Requirements:

Technology- Snowflake, Python, Reporting, Airflow, DBT, AWS, Azure, Data Modeling