Data Engineer

Ownership: • Build, enhance, and maintain production dbt models. • Extend existing dimensional models using established patterns. • Develop transformations across structured and semi-structured datasets. • Work across Silver and Gold layers within a medallion architecture. • Write and optimize complex SQL against large datasets. • Monitor and troubleshoot production data pipelines. • Investigate data quality issues and implement fixes. • Diagnose unfamiliar datasets, code, and upstream dependencies independently. • Participate in code reviews, testing, deployment, and documentation. • Coordinate with analytics engineers, data engineers, product teams, and business stakeholders during production issues. Technical Environment Core: • SQL • dbt • Databricks • AWS • GitHub Additional Environment: • Unity Catalog • PySpark • Distributed data processing • GitHub Actions / CI/CD • Dimensional modeling • Medallion architecture • Structured and semi-structured data • Production data pipelines • AI-assisted development, including GitHub Copilot Must-Haves: • 5+ years in Analytics Engineering, Data Engineering, or engineering-heavy Data Analytics. • Expert SQL with complex query development, optimization, and data modeling. • Extensive hands-on experience building and maintaining production dbt projects. • Experience with cloud-based data platforms, preferably AWS. • Modern data platform experience such as Databricks, Unity Catalog, Snowflake, or similar. • Experience supporting production data pipelines. • Strong troubleshooting and root-cause analysis skills. • Ability to investigate unfamiliar systems independently. • Strong communication across technical teams. • Evidence of writing maintainable, production-quality code. Nice-to-Haves: • Direct Databricks experience. • PySpark. • Unity Catalog. • GitHub Actions or comparable CI/CD. • Dimensional modeling. • Medallion architecture. • Business-critical reporting environments. • Comfortable using AI-assisted developer tooling