Snowflake, Python

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

Key Responsibilities: Data Platform & Solution Delivery - Lead the design and implementation of Snowflake-based data warehouse/lakehouse solutions aligned to business needs. - Build and optimize ELT/ETL pipelines using Python and dbt to deliver curated, analytics-ready datasets. - Develop and maintain Snowflake objects (schemas, tables, views, stages) and implement efficient data modeling patterns. - Drive performance tuning across Snowflake (warehouse sizing, clustering, query optimization) and transformation layers. Engineering Excellence & Reliability - Establish coding standards, reusable frameworks, and best practices for Python and dbt development. - Implement data quality checks, automated testing, and monitoring to ensure reliable and accurate data delivery. - Ensure secure data access patterns and support governance practices such as role-based access and auditing. Leadership & Collaboration - Mentor engineers, conduct code reviews, and guide the team on design decisions and implementation approaches. - Partner with stakeholders to translate requirements into technical designs, delivery plans, and measurable outcomes. - Support release planning, incident triage, and continuous improvements to reduce operational overhead. Minimum Qualifications: - BTECH, MTECH, MCA, MSC (or equivalent) in Computer Science, Engineering, or a related field. - 6–8 years of overall experience in data engineering / data platform development. - 6+ years of hands-on experience with Snowflake, including data modeling, performance tuning, and administration fundamentals. - 6+ years of hands-on experience with Python for building data pipelines, automation, and integrations. - Strong SQL skills and experience building reliable, maintainable transformations and datasets. - Proven experience delivering production-grade data solutions with attention to scalability, reliability, and maintainability.

Preferred Qualifications: - Strong hands-on experience with dbt (models, macros, tests, documentation, and deployment practices). - Experience designing modular transformation layers (staging/intermediate/marts) and implementing robust data quality frameworks. - Familiarity with orchestration and scheduling patterns for pipelines and transformations. - Experience with CI/CD practices for data engineering (version control workflows, automated testing, and release management). - Demonstrated ability to lead technical discussions, influence architecture decisions, and mentor team members effectively.