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., NetSuite, 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. Requirements 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, BigQuery, 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 (NetSuite, 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. #J-18808-Ljbffr