Analytics Engineer

J ob Summary The Data & Analytics team builds the reporting and data infrastructure that supports company-wide decision-making. The Analytics Engineer will own the layer between raw data and finished reporting, combining SQL data modeling with Power BI semantic models and dashboards. This hybrid role partners with the Sr.

Manager, Data & Analytics and business stakeholders across Finance, Operations, and other functions to build accurate, well-documented data models, support scheduled data pipelines, and troubleshoot refresh or pipeline issues as needed.

Key Responsibilities

Design, build, and maintain SQL views, staging tables, and fact/dimension models in our Azure SQL data warehouse, including deduplication and business-rule logic (e.g., status-based routing, multi-source reconciliation). Semantic models & reporting: Build and maintain Power BI semantic models — relationships, DAX measures, security roles — and the reports and dashboards built on top of them for business stakeholders and company-wide reporting.

Pipeline support: Share responsibility with the Sr. Manager, Data & Analytics for monitoring scheduled Azure Data Factory pipelines and Power BI dataset refreshes. Respond to failures and perform basic troubleshooting. Pipeline modifications: Make minor modifications to existing pipelines to support new or changing business requirements, as your familiarity with the tooling grows.

Business partnership: Partner with business SMEs to translate reporting requests and business logic (commission structures, revenue recognition, inventory rules, etc.) into accurate, well-documented data models. Documentation: Write and maintain documentation for data models, metric definitions, and report logic so that data lineage and ownership are clear beyond any one person.

Standards & quality: Follow and help evolve team standards for naming conventions, DAX style, and semantic model design as the team's practices mature. Continuous Improvement: Proactively identify opportunities for process improvements, optimize current data workflows, and incorporate new technologies or tools to enhance data analytics capabilities.

Knowledge and Skills Required Solid Power BI experience beyond report formatting — you've built semantic models from scratch and written DAX involving CALCULATE, filter context, and context transition, not just basic aggregations. Dimensional modeling fundamentals (star schemas, slowly changing dimensions). A track record of working directly with business stakeholders to translate ambiguous requirements or business rules into a working data model.

Strong attention to detail with data integrity — you double-check your joins and know how a bad join or an inclusive date boundary can quietly break a report. Preferred Exposure to an ERP or other core business system as a data source (order, invoicing, or GL data) - you understand that business rules, not just dates, often drive how records should be deduplicated or classified.

Understanding of ETL/ELT concepts and working knowledge of orchestration tools like Azure Data Factory or similar tools for automating data pipelines. Familiarity with Microsoft Fabric Administration and Environment (Lakehouses, Dataflows Gen2) — not required, but a plus given our platform direction. Basic Git/source control experience.

Python for data tasks.

Qualifications

Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field. Master’s degree is a plus. Minimum (three) 3+ years in a data-focused role (analytics engineer, BI developer, data analyst, or similar) with hands-on SQL work — comfortable with CTEs, window functions, and reading/writing complex, multi-source views.

Knowledge of data quality frameworks and data governance practices.