Snowflake and Pyspark Developer (Data Engineer) WALK-IN DRIVE 23rd May

Snowflake and Pyspark Developer (Data Engineer) Section I - Overview This is for a Development project using Snowflake and AWS. Technologies: Snowflake, PYSPARK, AWS Glue, Snowpipe, Snowpark using Python. Job Title: Snowflake and Pyspark Developer (Data Engineer) Coding: Mandatory Experience Level: - Min: 4 years - Max: 8 years Location: Hyderabad/Bangalore/Pune Work Type: Full-time Section II - Job Evaluation Evaluation based on the following topics: Topic of Evaluation / Skills Mandatory / Non-mandatory Percentage Snowflake, PYSPARK, AWS Glue, Snowpipe, Snowpark using Python Mandatory (Not Specified) SQL programming Mandatory (Not Specified) (Note: Percentage values should sum up to 100%) Section III - Job Requirements and Responsibilities Job Requirements Must-Have - Proficient in SQL programming (stored procedures, user defined functions, CTEs, window functions), Design and implement Snowflake data warehousing solutions, including data modelling and schema designing Snowflake - Able to source data from APIs, data lake, on premise systems to Snowflake. - Must have minimum 2 yrs experience in AWS Glue - Process semi structured data using Snowflake specific features like variant, lateral flatten - Experience in using Snowpipe to load micro batch data. - Experience in Snowpark using python. - Good knowledge of caching layers, micro partitions, clustering keys, clustering depth, materialized views, scale in/out vs scale up/down of warehouses. - Hands-on in creating reusable pipelines for handling SCD type1, type2 loads. - Ability to implement data pipelines to handle data retention, data redaction use cases. - Proficient in designing and implementing complex data models, ETL processes, and data governance frameworks. - Strong hands on in migration projects to Snowflake - Deep understanding of cloud-based data platforms and data integration techniques. - Skilled in writing efficient SQL queries and optimizing database performance. - Ability to develop and implementation of a real-time data streaming solution using Snowflake. Positive-to-Have - Good knowledge of caching layers, micro partitions, clustering keys, clustering depth, materialized views, scale in/out vs scale up/down of warehouses. Key Responsibilities - Analysis of business requirements - Involve in daily scrum calls and retrospection. - Involve in development of code using Snowflake , MS SQL according to the requirements. - Involve in Unit Testing and Bug fixing during testing phase. Section IV - Job Qualifications & Skills Soft Skills: - Excellent communication - Team collaboration - Documentation and knowledge sharing Snowflake and Pyspark Developer (Data Engineer) Section I - Overview This is for a Development project using Snowflake and AWS. Technologies: Snowflake, PYSPARK, AWS Glue, Snowpipe, Snowpark using Python. Job Title: Snowflake and Pyspark Developer (Data Engineer) Coding: Mandatory Experience Level: - Min: 4 years - Max: 8 years Location: Hyderabad/Bangalore/Pune Work Type: Full-time Section II - Job Evaluation Evaluation based on the following topics: Topic of Evaluation / Skills Mandatory / Non-mandatory Percentage Snowflake, PYSPARK, AWS Glue, Snowpipe, Snowpark using Python Mandatory (Not Specified) SQL programming Mandatory (Not Specified) (Note: Percentage values should sum up to 100%) Section III - Job Requirements and Responsibilities Job Requirements Must-Have - Proficient in SQL programming (stored procedures, user defined functions, CTEs, window functions), Design and implement Snowflake data warehousing solutions, including data modelling and schema designing Snowflake - Able to source data from APIs, data lake, on premise systems to Snowflake. - Must have minimum 2 yrs experience in AWS Glue - Process semi structured data using Snowflake specific features like variant, lateral flatten - Experience in using Snowpipe to load micro batch data. - Experience in Snowpark using python. - Good knowledge of caching layers, micro partitions, clustering keys, clustering depth, materialized views, scale in/out vs scale up/down of warehouses. - Hands-on in creating reusable pipelines for handling SCD type1, type2 loads. - Ability to implement data pipelines to handle data retention, data redaction use cases. - Proficient in designing and implementing complex data models, ETL processes, and data governance frameworks. - Strong hands on in migration projects to Snowflake - Deep understanding of cloud-based data platforms and data integration techniques. - Skilled in writing efficient SQL queries and optimizing database performance. - Ability to develop and implementation of a real-time data streaming solution using Snowflake. Positive-to-Have - Good knowledge of caching layers, micro partitions, clustering keys, clustering depth, materialized views, scale in/out vs scale up/d