Snowflake, Python

Responsibilities :

Key Responsibilities:

Lead end-to-end design and implementation of Snowflake-based data warehouse/lakehouse solutions aligned to business and analytics needs.

Architect and optimize data models (dimensional, normalized, and hybrid) to support reporting, BI, and advanced analytics use cases.

Develop and maintain Python-based data processing components, utilities, and orchestration logic to enable scalable data workflows.

Build and optimize Snowflake objects including databases, schemas, tables, views, stages, file formats, tasks, streams, and stored procedures where applicable.

Drive performance tuning across Snowflake (warehouse sizing, clustering, caching, query optimization) and Python workloads to meet SLAs.

Establish and enforce data engineering standards for code quality, testing, documentation, and release management.

Implement secure data access patterns, role-based access control, and governance practices to ensure compliance and auditability.

Collaborate with stakeholders to translate requirements into technical designs, delivery plans, and measurable outcomes.

Mentor team members through design reviews, pair programming, and technical guidance lead troubleshooting and root-cause analysis for production issues.

Coordinate with cross-functional teams to manage dependencies, risks, and delivery timelines for multiple concurrent initiatives. Minimum Qualifications:

Bachelor's or Master's degree (BTECH, MTECH, MCA, MSC) in Computer Science, Engineering, or a related field.

8-12 years of overall experience with strong ownership in data engineering and platform delivery.

8+ years of hands-on experience with Snowflake, including data modeling, performance optimization, and production support.

8+ years of strong Python development experience for data engineering use cases (data processing, automation, and integration).

Proven ability to lead technical design discussions, perform code reviews, and guide engineering best practices.

Strong SQL skills and experience building reliable, maintainable data pipelines and datasets for analytics consumption.

Additional Responsibilities:

Preferred Qualifications:

Experience leading architecture for enterprise-scale Snowflake implementations, including multi-environment setup and workload isolation strategies.

Strong experience with Python data engineering ecosystems (e.g., Pandas, PySpark) and building reusable frameworks/utilities.

Expertise in orchestration and scheduling patterns for data workflows, including dependency management and monitoring.

Experience implementing data quality checks, automated testing strategies, and observability for pipelines and datasets.

Familiarity with CI/CD practices for data platforms, including version control, automated deployments, and environment promotion.

Strong stakeholder management skills with the ability to communicate trade-offs, influence decisions, and align teams on technical direction.

Technical and Professional Requirements:

Technology- Data on Cloud-DataStore- Snowflake Technology- - Python