Data Analytics Engineer

Benefits: Dental insurance Health insurance Vision insurance Hi, We are actively seeking qualified candidates for the following position for our client, who is an industry leader: Data Analytics Engineer Location: Houston TX (4 days in office) Type: Full Time This role is a hybrid, bridging the gap between data engineering and business intelligence. You'll spend part of your time writing and optimizing SQL — building staging tables, views, and fact/dimension models — and part of your time building semantic models and reports in Power BI. Keeping an eye on our scheduled data pipelines, you’ll handle first-line troubleshooting when something fails. You'll work closely with the Sr. Manager, Data & Analytics, and with business stakeholders across Finance, Operations, and other functions who depend on accurate, well-modeled data. This is a great fit for someone who wants breadth — real ownership across the data stack — on a team small enough that your work visibly matters. Key Responsibilities · Data modeling & SQL: 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 · 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. · 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. Knowledge of data quality frameworks and data governance practices. Qualifications Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field. Master’s degree is a plus. 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. Flexible work from home options available.