Data Engineer (Snowflake & DBT)

- SQL: Expert-level mastery of SQL (complex joins, window functions, and query optimization techniques). - Programming Skills: Strong working knowledge of Python or Scala for building custom data utilities or ingestion scripts. - Version Control: Strong command of Git workflows for collaborative data modeling and code reviews.

- Pipeline Development (ETL/ELT): Build scalable ingestion pipelines to extract data from various enterprise source systems (such as ERPs like SAP or Oracle, CRM platforms, and operational databases) and load it into Snowflake. - Performance Optimization: Troubleshoot and fine-tune complex SQL queries, dbt models, and Snowflake warehouse utilization to improve execution speeds and manage costs efficiently. - Data Quality & Governance: Establish rigorous data quality checks, testing frameworks (using dbt test configurations), and data monitoring patterns to ensure enterprise data reliability and lineage accuracy.

- dbt (Data Build Tool): Advanced proficiency with dbt Core or dbt Cloud, macros, packages, and custom testing strategies. - SQL: Expert-level mastery of SQL (complex joins, window functions, and query optimization techniques). - Programming Skills: Strong working knowledge of Python or Scala for building custom data utilities or ingestion scripts.

- Experience with orchestration tools such as Apache Airflow, Prefect, or Dagster. - Familiarity with cloud platforms (AWS, Azure, or GCP) and cloud-native data security principles.

Role Overview We are seeking a highly skilled Senior Data Engineer with deep expertise in Snowflake and dbt (Data Build Tool) to join our data analytics team. In this role, you will be responsible for designing, building, and optimizing robust data pipelines, transforming raw data into clean, actionable data models, and establishing scalable data warehousing architectures. You will collaborate closely with business analysts, data scientists, and business stakeholders to drive data-driven decision-making across our manufacturing, supply chain, and operational workflows. Key Responsibilities

Key Responsibilities - Data Modeling & Transformation: Design, develop, and maintain robust data transformation pipelines using dbt (Data Build Tool) to implement version-controlled, tested, and documented data models. - Data Warehousing Architecture: Architect, manage, and optimize enterprise-scale data warehouses within Snowflake, ensuring optimal storage, compute sizing, clustering, and data sharing strategies.

- Performance Optimization: Troubleshoot and fine-tune complex SQL queries, dbt models, and Snowflake warehouse utilization to improve execution speeds and manage costs efficiently. - Data Quality & Governance: Establish rigorous data quality checks, testing frameworks (using dbt test configurations), and data monitoring patterns to ensure enterprise data reliability and lineage accuracy. - Collaboration & CI/CD: Implement and maintain best practices for modern data stack workflows, utilizing Git and CI/CD pipelines (e.g., GitHub Actions, GitLab CI) for seamless deployment of data models.

- Snowflake: In-depth, production-level experience with Snowflake architecture, Snowpipe, tasks, streams, cloning, and secure data sharing. - dbt (Data Build Tool): Advanced proficiency with dbt Core or dbt Cloud, macros, packages, and custom testing strategies. - SQL: Expert-level mastery of SQL (complex joins, window functions, and query optimization techniques).

- Prior experience in heavy manufacturing, building materials, logistics, or infrastructure domains. - Certifications such as SnowPro Core / Advanced or dbt Certified Developer. - Experience with orchestration tools such as Apache Airflow, Prefect, or Dagster.

- Data Warehousing Architecture: Architect, manage, and optimize enterprise-scale data warehouses within Snowflake, ensuring optimal storage, compute sizing, clustering, and data sharing strategies. - Pipeline Development (ETL/ELT): Build scalable ingestion pipelines to extract data from various enterprise source systems (such as ERPs like SAP or Oracle, CRM platforms, and operational databases) and load it into Snowflake. - Performance Optimization: Troubleshoot and fine-tune complex SQL queries, dbt models, and Snowflake warehouse utilization to improve execution speeds and manage costs efficiently.

- Certifications such as SnowPro Core / Advanced or dbt Certified Developer. - Experience with orchestration tools such as Apache Airflow, Prefect, or Dagster. - Familiarity with cloud platforms (AWS, Azure, or GCP) and cloud-native data security principles.

- Data Modeling & Transformation: Design, develop, and maintain robust data transformation pipelines using dbt (Data Build Tool) to implement version-controlled, tested, and documented data models. - Data Warehousing Architecture: Architect, manage, and optimize enterprise-scale data warehouses within Snowflake, ensuring optimal storage, compute sizing, clustering, and data sharing strategies. - Pipeline Development (ETL/ELT): Build scalable ingestion pipelines to extract data from various enterprise source systems (such as ERPs like SAP or Oracle, CRM platforms, and operational databases) and load it into Snowflake.

- Core Technical Stack: - Snowflake: In-depth, production-level experience with Snowflake architecture, Snowpipe, tasks, streams, cloning, and secure data sharing. - dbt (Data Build Tool): Advanced proficiency with dbt Core or dbt Cloud, macros, packages, and custom testing strategies.