Data Analytics Engineer

DEXTER TECHNOLOGIES INC•Houston, TX

3h•$130,000 - $140,000•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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.

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).
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.
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.
Make minor modifications to existing pipelines to support new or changing business requirements, as your familiarity with the tooling grows.
Partner with business SMEs to translate reporting requests and business logic (commission structures, revenue recognition, inventory rules, etc.) into accurate, well-documented data models.
Write and maintain documentation for data models, metric definitions, and report logic so that data lineage and ownership are clear beyond any one person.
Follow and help evolve team standards for naming conventions, DAX style, and semantic model design as the team's practices mature.
Proactively identify opportunities for process improvements, optimize current data workflows, and incorporate new technologies or tools to enhance data analytics capabilities.