Data Analytics Engineer needed in Houston, TX (hybrid) to connect data engineering with business intelligence using Azure and Power BI.

Design, build, and maintain SQL views, staging tables, and fact/dimension models in an Azure SQL data warehouse.
Implement deduplication and business-rule logic (including status-based routing and multi-source reconciliation).
Develop and maintain Power BI semantic models including relationships, DAX measures, and security roles.
Create reports and dashboards that run on top of semantic models for business stakeholders and company-wide reporting.
Monitor scheduled Azure Data Factory pipelines and Power BI dataset refreshes in collaboration with the Sr. Manager, Data & Analytics.
Respond to pipeline and refresh failures and perform first-line troubleshooting.
Make minor modifications to existing pipelines to meet new or changing business requirements as familiarity with the stack grows.
Partner with business SMEs to convert reporting requests and business logic (examples: commission structures, revenue recognition, inventory rules) into accurate, documented data models.
Write and maintain documentation for data models, metric definitions, and report logic to clarify lineage and ownership.
Help evolve team standards for naming conventions, DAX style, and semantic model design.
Identify process improvement opportunities, optimize data workflows, and incorporate new technologies to enhance analytics capabilities.

3+ years in a data-focused role (analytics engineer, BI developer, data analyst, or similar) with hands-on SQL work.
Comfort with CTEs, window functions, and reading/writing complex, multi-source views.
Strong Power BI experience including building semantic models from scratch and writing DAX using CALCULATE, filter context, and context transition.
Dimensional modeling fundamentals: star schemas and slowly changing dimensions.
Proven experience working with business stakeholders to translate ambiguous requirements or business rules into a working data model.
High attention to detail for data integrity (for example, validating joins and avoiding issues like inclusive date boundaries that can break reports).

Exposure to an ERP or other core business system as a data source (order, invoicing, GL data), understanding that business rules often drive deduplication and classification.
Understanding of ETL/ELT concepts and working knowledge of orchestration tools such as Azure Data Factory or similar.
Familiarity with Microsoft Fabric Administration and Environment (Lakehouses, Dataflows Gen2) as a plus (not required).
Basic Git/source control experience.
Python for data tasks.
Knowledge of data quality frameworks and data governance practices.

Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field

Data Analytics Engineer

Job Description