Snowflake Developer

As a Snowflake Data Engineer at our company, you will play a crucial role in designing and implementing scalable data engineering and analytics solutions on the Snowflake Data Cloud. Leveraging Snowpark (Python API), you will build efficient data pipelines, transformation logic, and advanced analytics workflows directly within Snowflake, contributing to modern data platforms. Key Responsibilities: - Data Engineering & Snowpark Development: - Design and develop data pipelines (ETL/ELT) using Snowflake, SQL, and Python (Snowpark). - Build data transformation logic using Snowpark DataFrame API. - Develop stored procedures, UDFs, and UDTFs in Python. - Enable data processing and analytics workflows directly within Snowflake. - Data Modeling & Optimization: - Design and maintain data models and schema architecture. - Optimize query performance and cost efficiency. - Implement data governance, security, and RBAC policies. - Advanced Data Processing & Analytics: - Perform large-scale data transformation and processing using Python. - Build data science/ML workflows using Snowpark where applicable. - Support feature engineering, model scoring, and analytics pipelines. - Integration & Automation: - Integrate data from sources such as Cloud storage, SFTP, APIs, and databases. - Automate workflows using tasks, pipelines, and orchestration tools. - Performance Tuning & Troubleshooting: - Monitor and resolve data quality issues, performance bottlenecks, and pipeline failures. - Optimize warehouse usage and compute scaling. - Collaboration & Delivery: - Work with data architects, business analysts, engineering, and QA teams. - Translate business requirements into technical solutions. - Participate in Agile ceremonies and deliver end-to-end solutions. Required Skills & Experience: Core Skills: - Strong experience in Snowflake, SQL, and Performance Tuning. - Hands-on experience in Python. - Working experience with Snowpark for data pipelines. - Expertise in ETL/ELT design and implementation. - Solid understanding of data warehousing concepts. Technical Skills: - SQL: Complex queries, stored procedures, optimization. - Python: Data processing, automation, scripting. - Snowflake: Data modeling, pipelines, security. Cloud & Tools: - Exposure to at least one: AWS, Azure, GCP. - Experience with CI/CD tools, Git. - Orchestration tools like Airflow, dbt (optional). Preferred Qualifications: - Snowflake certifications. Soft Skills: - Strong analytical and problem-solving skills. - Ability to work in cross-functional environments. - Good communication for stakeholder interaction. - Agile mindset with a focus on delivery and quality. Typical Experience Range: - 4-10 years in Data Engineering/Snowflake ecosystem. - 2+ years working on Python-based data processing or Snowpark. As a Snowflake Data Engineer at our company, you will play a crucial role in designing and implementing scalable data engineering and analytics solutions on the Snowflake Data Cloud. Leveraging Snowpark (Python API), you will build efficient data pipelines, transformation logic, and advanced analytics workflows directly within Snowflake, contributing to modern data platforms. Key Responsibilities: - Data Engineering & Snowpark Development: - Design and develop data pipelines (ETL/ELT) using Snowflake, SQL, and Python (Snowpark). - Build data transformation logic using Snowpark DataFrame API. - Develop stored procedures, UDFs, and UDTFs in Python. - Enable data processing and analytics workflows directly within Snowflake. - Data Modeling & Optimization: - Design and maintain data models and schema architecture. - Optimize query performance and cost efficiency. - Implement data governance, security, and RBAC policies. - Advanced Data Processing & Analytics: - Perform large-scale data transformation and processing using Python. - Build data science/ML workflows using Snowpark where applicable. - Support feature engineering, model scoring, and analytics pipelines. - Integration & Automation: - Integrate data from sources such as Cloud storage, SFTP, APIs, and databases. - Automate workflows using tasks, pipelines, and orchestration tools. - Performance Tuning & Troubleshooting: - Monitor and resolve data quality issues, performance bottlenecks, and pipeline failures. - Optimize warehouse usage and compute scaling. - Collaboration & Delivery: - Work with data architects, business analysts, engineering, and QA teams. - Translate business requirements into technical solutions. - Participate in Agile ceremonies and deliver end-to-end solutions. Required Skills & Experience: Core Skills: - Strong experience in Snowflake, SQL, and Performance Tuning. - Hands-on experience in Python. - Working experience with Snowpark for data pipelines. - Expertise in ETL/ELT design and implementation. - Solid understanding of data warehousing concepts. Technical Skills: - SQL: Complex queries, sto