Senior Data Analytics Engineer

Responsibilities

• Build the core analytics data model – Design and maintain scalable fact and dimension models in our cloud data warehouse that support enterprise reporting and analysis.
• Create a trusted reporting layer across business systems – Integrate and model data from ERP, financial, project management, and operational platforms (e.g., Net Suite, Procore, Epicor, Snowflake, Timestream) to create a consistent source of truth.
• Preserve critical business context across systems – Identify where important financial or operational detail is lost in source systems or handoffs, and design data structures that retain the fidelity needed for analysis.
• Establish modeling standards and analytics best practices – Help define scalable approaches to naming, structure, metric logic, testing, and maintainability across the analytics environment.
• Deliver meaningful reporting and dashboards – Build and maintain dashboards and semantic models in our BI tooling that give stakeholders visibility into financial performance, project execution, and operational KPIs.
• Partner directly with stakeholders to solve ambiguous problems – Translate real business questions – such as cost overruns, efficiency gaps, and project performance – into clear datasets, metrics, and reporting solutions.
• Support planning and forecasting workflows – Maintain and improve planning and budgeting environments to help align planning, forecasting, and actual performance.
• Standardize KPIs across teams – Work with Finance, IT, Continuous Improvement, Operations, and other functions to define, document, and govern consistent metrics across the organization.

• Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related field – equivalent experience considered.
• 5+ years of experience in analytics engineering, data engineering, BI, or a related data role.
• Expert-level SQL – Comfort with complex joins, window functions, CTEs, set-based logic, and query optimization across large datasets.
• Modern cloud data warehousing – Strong hands‑on experience with at least one modern cloud warehouse (Snowflake, Big Query, Databricks, Redshift, or similar). Snowflake experience preferred.
• Dimensional modeling – Deep understanding of star/snowflake schemas, slowly changing dimensions, and scalable analytics structures.
• Modern BI / semantic-layer tooling – Hands‑on experience with Power BI, Tableau, Looker, Sigma, Grafana, or similar. Strong DAX or equivalent semantic-layer skills preferred.
• Experience integrating data from ERP, financial, project management, or other operational systems (Net Suite, Epicor, Procore, SAP, Oracle, Workday, or similar).
• Problem‑solving – Strong analytical skills and the ability to work through loosely defined business questions while managing multiple priorities.
• Communication – Excellent communication skills to effectively interact with both technical and non‑technical stakeholders.
• Cross‑functional comfort – Thrives in an environment where priorities span finance, operations, and analytics.