Assistant Insurance Data Analytics Jobs in Houston, TX

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Hi,

We are actively seeking qualified candidates for the following position for our client, who is an industry leader:

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.

• 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.

• 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.

• 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.

• 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.