Sr. Data Engineer

Key Responsibilities Design, develop, and maintain end-to-end data pipelines using SnapLogic (preferred) or comparable ETL/ELT platforms such as Informatica, Talend, MuleSoft, or Azure Data Factory. Build, optimize, and document dbt models, including sources, staging, intermediate, and mart layers, following software engineering best practices (version control, code review, CI/CD). Architect and manage data workloads in Snowflake, including schema design, performance tuning, clustering, role-based access control, and cost optimization. Collaborate with data analysts and scientists to translate complex business requirements into reliable data models and transformation logic. Establish and enforce data quality standards through testing frameworks (e.g., dbt tests, Great Expectations) and monitoring dashboards. Participate in architectural reviews, contribute to engineering standards, and mentor junior engineers. Identify and resolve performance bottlenecks across the data stack, from ingestion through consumption. Support deployment pipelines and contribute to infrastructure-as-code practices for data platform components. Required Qualifications Experience 6+ years of overall software or data engineering experience, with at least 3 years focused on data pipeline development and cloud data platforms. ETL / Integration Hands-on experience with one or more ETL/ELT tools (SnapLogic strongly preferred; Informatica, Talend, MuleSoft, or Azure Data Factory also considered). dbt Proven proficiency with dbt Core or dbt Cloud — model development, Jinja macros, ref/source dependencies, incremental models, snapshots, and automated testing. Snowflake Strong working knowledge of Snowflake: virtual warehouses, dynamic tables, time travel, data sharing, and SQL performance optimization. SQL Expert-level SQL skills with experience writing complex analytical queries, window functions, and query optimization.