Snowflake, Python

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

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. 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.