Top Remote Analysis Reporting Jobs in Houston, TX

Leads and scales a global data engineering and analytics team while remaining hands-on with architecture, pipelines, data quality, dimensional modeling, dashboards, reporting, and customer-facing deliverables. Owns the analytical data platform supporting clinical, operational, product, financial, and customer data. Partners with cross-functional stakeholders, investigates complex data issues, improves platform reliability and security, and mentors engineers.

Maintains and updates GIS fiber network data for rural broadband projects using ArcGIS Pro and QGIS. Digitizes and edits vector data, creates network maps and reports, transforms geospatial datasets, applies geoprocessing tools, and performs quality assurance checks. Collaborates with engineers, project managers, and GIS teams to deliver accurate mapping work on schedule.

Own client-facing analytics and attribution: establish baselines, implement tracking and lead-to-sale matching, run holdout and lift experiments, quantify cost avoidance, build cross-client benchmarks, and translate findings into clear ROI narratives that inform pricing, product, and renewal conversations.

Build, maintain, and monitor ETL/ELT pipelines in the Microsoft Fabric ecosystem. Implement medallion architecture, transform raw data into analytics-ready models, ensure pipeline reliability and data quality, troubleshoot failures, optimize performance, and support CI/CD and version control for data artifacts.

Builds and maintains analytical data foundations for lending, underwriting, pricing, originations, collections, and portfolio analytics. Develops reusable SQL assets, dimensional models, data marts, transformation workflows, reporting solutions, and business intelligence products using SQL, Python, dbt, Git, Snowflake, Power BI, and Sigma. Partners with technical teams, analysts, and business stakeholders to integrate data, standardize metrics, automate testing, and deliver reliable, documented, scalable analytical assets.

Lead definition and evolution of cloud data architecture and analytics platform using a lakehouse approach. Design secure, scalable AWS data platforms, medallion data layers, data modeling standards, ETL/ELT and streaming pipelines, governance, observability, and AI/ML enablement. Drive tool selection, cross-team architecture reviews, mentor engineers, and enforce data security, compliance, and operational excellence across the organization.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Lead a high-impact analytics function partnering with clinical and business leaders to apply advanced analytics, modeling, and BI to improve participant outcomes, operations, and financial performance. Manage and mentor analysts, design analyses and models, build stakeholder relationships, and communicate actionable insights to influence enterprise strategy.

Leads complex, cross-functional business intelligence initiatives from strategy and scoping through delivery. Partners with senior leaders across Sales, Marketing, Finance, and Operations to build scalable data products and actionable insights. Establishes standards for metrics, data modeling, governance, quality, and storytelling; designs data infrastructure using modern tools such as dbt. Drives data literacy, mentors analysts, contributes to analytics roadmaps, and ensures data assets are trusted, discoverable, secure, and fit for executive decision-making.

Designs, builds, documents, tests, and maintains large-scale data pipelines across platforms and sources. Performs ETL and ELT while ensuring data governance, security, quality, validation, anomaly detection, and compliance. Collaborates with architects, analysts, and stakeholders to translate business needs into technical solutions, develops data models, and mentors junior data engineers.