Top Senior Analytics Jobs in Houston, TX

The Director, Research - Power Markets is responsible for leading the QA/QC process for long-term forecasting, advising leadership, and ensuring market insights are effectively integrated into forecasts.

The Sr Manager of Payor Performance Analytics drives financial, analytical, and operational components of payer contracting, overseeing reimbursement modeling and ensuring successful contract negotiations and implementation.

Designs, builds, and operates ELT data pipelines and analytical datasets for Platform Analytics. Curates and models source data into conformed datasets, ensures performance, reliability, and cost efficiency, contributes to target-state architecture, mentors junior engineers, and uses AI coding tools to accelerate development and delivery.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Lead a team of analysts and data scientists embedded in sales to build rigorous pilot analyses, create executive-ready business cases, expand consultative analytics across the partner lifecycle, and publish industry insights while coaching and scaling the Solutions Analytics function.

This role drives data-driven decision-making in product analytics, collaborating with cross-functional teams to generate actionable insights and evaluate new launches to enhance platform growth.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

The Senior Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

Lead CX analytics strategy and operations by standardizing metrics, enabling self-service analytics, and embedding AI in the analytics lifecycle to enhance decision-making processes.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.

The Senior Reporting & Analytics Consultant leads customer-focused reporting and analytics engagements, developing solutions, supporting implementations, and training clients, while ensuring project success.

The Staff Analytics Engineer will own the Financial Subledger Data Platform, build dbt models, implement data quality controls, and mentor a junior engineer, ensuring high operational reliability and cross-functional collaboration.

The role involves collaborating with business users on analytics, processing data, and applying statistical methods to analyze large datasets and present insights.

Lead the marketplace analytics by optimizing monetization strategies, monitoring performance, creating dashboards, and analyzing supply-demand dynamics. Collaborate cross-functionally to drive engagement and profitability.

The Business Strategist will align client strategies with data analysis solutions, managing projects and analytics to provide insights and recommendations for business growth.

The Power BI Architect will lead BI platform design and governance, ensuring scalable solutions and guiding BI Engineers, while collaborating with Azure teams.

Lead design and execution of enterprise information governance: records and lifecycle management, retention, defensible disposal, policy translation, training, compliance (GDPR/cross‑border), and operationalizing IG controls across systems (M365, collaboration tools). Support audits, eDiscovery, data classification technology deployment, and align IG with privacy, security, AI, and digital initiatives.

Design and automate partner-facing reporting systems, transform complex healthcare data into clear insights, present findings to external stakeholders, support partner success, and collaborate cross-functionally to scale analytics workflows and uncover operational and clinical trends.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

The Analytics Manager will lead analytics for the Payments and Identity lifecycle, manage a team of analysts, develop KPIs, and ensure data quality while collaborating with cross-functional teams.

The analyst will own planning for launches, build forecasting models, partner with stakeholders, and drive data-driven decisions using AI and analytics.

Perform financial due diligence data analytics for middle-market M&A: obtain, validate, normalize, analyze transaction data (revenue, costs, inventory), produce dashboards and visualizations, identify trends/outliers, and communicate insights to deal teams and clients.