Job overview We are looking for a highly experienced senior data engineer with deep expertise in Snowflake and strong hands on knowledge of ETL and data warehousing concepts including slowly changing dimensions SCD The ideal candidate will be well versed in data engineering principles such as data ingestion archival and backup strategies and bring a strong foundation in SQL and data modeling Familiarity with SAP data structures and MS Excel for data reconciliation and analysis is a strong plus Key responsibilities include designing building and maintaining robust ETL pipelines using Snowflake and other modern data tools and platforms architecting scalable data warehouse solutions ensuring optimal performance and alignment with business needs implementing slowly changing dimensions SCD types 1 and 2 star and snowflake schema models and advanced dimensional modeling techniques leading efforts around data ingestion from various source systems including SAP flat files and APIs defining and enforcing data archival retention and backup strategies in line with business and compliance requirements performing data profiling cleansing validation and reconciliation to ensure data quality collaborating with cross functional teams including data analysts business stakeholders and data scientists to gather requirements and deliver reliable data solutions troubleshooting complex data pipeline and performance issues in Snowflake and related environments utilizing MS Excel for data comparison reconciliation and stakeholder reporting tasks analyzing and extracting data from SAP tables and supporting integration with enterprise data platforms Required skills and qualifications include eight to ten years of experience in data engineering ETL development and data warehousing strong hands on expertise with Snowflake including data modeling query optimization user and role management and Snowpipe or similar ingestion frameworks solid understanding and practical implementation experience with ETL processes data transformations and dimensional modeling expertise in implementing slowly changing dimensions SCD type 1 and 2 and other warehouse design patterns deep knowledge of data engineering concepts such as data ingestion data archival backup strategies and data lifecycle management proficiency in SQL and at least one scripting language such as Python or shell scripting experience with data pipeline orchestration tools such as Airflow dbt Informatica or similar familiarity with SAP table structures and SAP data extraction methodologies and strong skills in MS Excel for data analysis and stakeholder reporting
