Job
Description
About Company We provide companies with innovative technology solutions for everyday business problems. Our passion is to help clients become intelligent, information-driven organizations. By embedding fact-based decision-making into daily operations, we optimize processes and outcomes. Experience 3 to 10 Years Required Qualifications: Data Engineering Skills 3–5 years of experience in data engineering, with hands-on experience in Snowflake and basic to intermediate proficiency in dbt. Capable of building and maintaining ELT pipelines using dbt and Snowflake, with guidance on architecture and best practices. Understanding of ELT principles and foundational knowledge of data modeling techniques (preferably Kimball/Dimensional). Intermediate experience with SAP Data Services (SAP DS), including extracting, transforming, and integrating data from legacy systems. Proficient in SQL for data transformation and basic performance tuning in Snowflake (e.g., clustering, partitioning, materializations). Familiar with workflow orchestration tools like dbt Cloud, Airflow, or Control M. Experience using Git for version control and exposure to CI/CD workflows in team environments. Exposure to cloud storage solutions such as Azure Data Lake, AWS S3, or GCS for ingestion and external staging in Snowflake. Working knowledge of Python for basic automation and data manipulation tasks. Understanding of Snowflake's role-based access control (RBAC), data security features, and general data privacy practices like GDPR. Key Responsibilities Design and build robust ELT pipelines using dbt on Snowflake, including ingestion from relational databases, APIs, cloud storage, and flat files. Reverse-engineer and optimize SAP Data Services (SAP DS) jobs to support scalable migration to cloud-based data platforms. Implement layered data architectures (e.g., staging, intermediate, mart layers) to enable reliable and reusable data assets. Enhance dbt/Snowflake workflows through performance optimization techniques such as clustering, partitioning, query profiling, and efficient SQL design. Use orchestration tools like Airflow, dbt Cloud, and Control-M to schedule, monitor, and manage data workflows. Apply modular SQL practices, testing, documentation, and Git-based CI/CD workflows for version-controlled, maintainable code. Collaborate with data analysts, scientists, and architects to gather requirements, document solutions, and deliver validated datasets. Contribute to internal knowledge sharing through reusable dbt components and participate in Agile ceremonies to support consulting delivery. Skills: sql,ci/cd,dbt,data engineering,azure data lake,gcs,git,ci,sap data services,airflow,aws s3,snowflake,python Show more Show less
