Analytics Engineer

About This Role

Designing and implementing modern data architecture within an advanced, AI-driven environment is the core of this role. You will play a foundational part in shaping the enterprise semantic layer, architecting scalable data marts, and bridging the gap between raw operational data and machine-readable models that power both business intelligence and cutting-edge generative AI querying tools.

Responsibilities

• Design, deploy, and maintain modular dbt models that transform replicated ERP data into clean, well-governed schemas within Snowflake
• Architect resilient semantic models that streamline self-service business intelligence while enabling natural-language querying via large language models
• Build and scale dimensional data marts focused on financial tracking, power generation performance, and revenue variance analysis
• Deliver the data foundation and optimized queries required for interactive Hex dashboards and executive reporting assets
• Implement rigorous dbt testing, schema validations, and data quality frameworks to safeguard pipeline reliability and regulatory compliance
• Utilize AI coding assistants such as Claude Code, ChatGPT, and GitHub Copilot to accelerate code scaffolding, SQL tuning, and documentation while maintaining strict code review standards
• Partner cross-functionally with data engineers, financial analysts, and trading teams to translate business metrics into production-ready analytical products

Requirements

• Bachelor's degree in Computer Science, Mathematics, Information Systems, Engineering, or a related quantitative field
• 5+ years of professional experience in an Analytics Engineering, Data Engineering, or BI Engineering role managing production dbt workflows
• Advanced proficiency in SQL, including deep experience with window functions, CTEs, and query optimization techniques
• In-depth practical knowledge of Snowflake architecture, covering performance tuning, clustering, and cost-conscious model design
• Strong expertise building modular data layers, macros, and automated testing frameworks in dbt Core or dbt Cloud
• Proficiency in Python for workflow automation, data pipeline scripting, and custom testing logic
• Demonstrated track record modeling datasets specifically for modern BI platforms or collaborative notebook tools such as Hex
• Hands-on experience with Git-based version control and CI/CD pipelines (such as GitLab CI or Azure DevOps)
• Working experience integrating LLM coding assistants into development cycles with a strong focus on data privacy and code reliability

Nice to have:

• Official dbt Developer or Analytics Engineer Certification
• Hands-on experience developing Snowflake semantic models (Cortex Analyst) or MetricFlow / dbt Semantic Layer definitions