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.
