The Role
Design, build, and maintain SQL-based ETL/ELT models in Azure SQL and Power BI semantic models and dashboards. Monitor and troubleshoot data pipelines and dataset refreshes, partner with business stakeholders to translate requirements, document data lineage and metrics, and enforce data-quality and modeling standards.
Summary Generated by Built In
Job 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
- 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
- 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.
Skills Required
- Power BI semantic model development and advanced DAX (CALCULATE, filter context, context transition)
- Hands-on SQL work (CTEs, window functions, complex multi-source views) and data modeling
- Experience with Azure SQL data warehouse and building staging/fact/dimension models
- Dimensional modeling fundamentals (star schemas, slowly changing dimensions)
- Proven experience translating ambiguous business requirements into data models
- Strong attention to data integrity and validation (preventing bad joins, date boundary issues)
- Bachelor's degree in Computer Science, Data Science, Information Systems, or related field
- Minimum 3+ years in a data-focused role (analytics engineer, BI developer, data analyst, or similar)
- Knowledge of data quality frameworks and data governance practices
- Exposure to ERP or core business system data sources (orders, invoicing, GL)
- Understanding of ETL/ELT concepts and working knowledge of orchestration tools (e.g., Azure Data Factory)
- Familiarity with Microsoft Fabric (Lakehouses, Dataflows Gen2)
- Basic Git/source control experience
- Python for data tasks
- Master's degree (optional, a plus)
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The Company
What We Do
National Trench Safety (NTS) specializes in the rental and sale of trench safety, traffic safety and related equipment and services. NTS also provides OSHA compliant training courses for customers, as well as engineering services for site specific trench safety plans and site specific traffic safety plans. NTS has a national footprint with over 67 branch locations from the East Coast to the West Coast in addition to its corporate headquarters in Houston, Texas.
