Top Senior Analysis Reporting Jobs in Dallas, TX

Designs and evolves an enterprise Azure-based Lakehouse platform using Microsoft Fabric and Azure Databricks. Builds metadata-driven ingestion, ELT, CDC, orchestration, Spark/PySpark transformations, semantic models, and governed Bronze, Silver, and Gold data layers. Integrates enterprise systems across ERP, CRM, construction, manufacturing, HR, and operations while establishing standards for data quality, lineage, security, observability, performance, and cost optimization. Partners with stakeholders to deliver trusted data products and provides technical leadership across architecture and engineering.

Analyzes healthcare, financial, and utilization data to identify inefficiencies, support business decisions, build and maintain dashboards, develop analytical models, and communicate actionable insights to technical and non-technical stakeholders.

Analyzes market pricing, historical trends, costs, bids, profitability, and contract rate terms. Maintains pricing databases, researches discrepancies, manages proposal updates, and produces recurring and ad hoc reports for high-profile accounts. The role also performs complex Excel queries and formulas, KPI reporting, exception troubleshooting, and trend analysis while supporting executives and strategic initiatives.

Provides customized healthcare operations analytics using Vizient and hospital data. Analyzes performance, costs, productivity, utilization, outcomes, supply use, and benchmarks; develops reports and actionable insights for hospital leaders. The role facilitates client meetings, translates findings into strategic and operational recommendations, ensures reporting accuracy, and contributes to mentoring and team best practices.

Senior Data Analyst cleans and prepares data, builds reports and dashboards using Power Platform, Excel, DAX, M, R or Python, develops data models and predictive/ML solutions, supports client/internal projects, and may lead others or assist business development.

Designs and governs enterprise data architecture for a banking organization. Responsibilities include developing data migration and streaming strategies, creating data inventories, maintaining architecture documentation, building data models for databases and AI/ML applications, establishing modeling standards, communicating architecture strategies, and coaching staff. Requires extensive experience with data management, data warehouses, ETL, streaming, cloud concepts, architecture, integration, and data platform technologies.

Leads enterprise-wide member, consumer, marketplace, and competitor research across quantitative, qualitative, and voice-of-member channels. Directs research strategy, governance, methodologies, insights reporting, budgets, vendors, and executive presentations. Builds and manages a high-performing research team, translates business objectives into actionable recommendations, oversees research infrastructure initiatives, and ensures compliance with risk policies. Partners cross-functionally to influence strategic decisions and improve member-centric business outcomes.

Leads pricing analytics initiatives, develops strategies for risk-based pricing, collaborates with leaders, prioritizes analytics projects, and enhances customer experience.

Designs, develops, and maintains scalable AWS data platforms, data lakes, and pipelines. Builds workflows with Glue, S3, Lambda, and Step Functions; implements governance and access controls through Lake Formation; automates infrastructure with Terraform; and develops Python and Boto3 integrations. Manages Snowflake platforms, CI/CD deployments, testing, monitoring, troubleshooting, and production optimization using CloudWatch and Splunk. Collaborates with engineering and architecture stakeholders while using AI-assisted development tools.

Leads BI, analytics, and AI initiatives that transform energy market and customer data into dashboards, automated insights, predictive analyses, and strategic recommendations. The role supports pricing, acquisition, retention, marketing, finance, and customer experience decisions through Python, SQL, Tableau, Power BI, generative BI, experimentation, and machine learning. Responsibilities include building executive reporting, evaluating AI-enhanced BI platforms, analyzing customer lifecycle performance, mentoring analysts, and partnering with cross-functional business and technical teams.