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.