Top Senior Analysis Reporting Jobs in Dallas, TX

Leads development, validation, implementation, and monitoring of credit scorecards, loss forecasting, collection, recovery, and pricing models for auto lending. Provides strategic recommendations balancing growth, profitability, risk, and regulatory compliance. Partners with Pricing, Risk, Finance, and senior leadership, translating complex statistical analyses into actionable business insights. Uses large-scale data and advanced analytics to monitor portfolio performance, identify risks, and improve forecasting accuracy.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

As a Principal Data Analyst, you'll innovate, build and maintain data solutions using scripting languages and cloud services to tackle business problems, providing insights and managing data quality.

Designs and maintains SQL/Python data pipelines, warehouse models, and Power BI dashboards supporting finance planning, forecasting, budgeting, and book close. Owns solution architecture from requirements through deployment, validates data and systems, resolves process and reliability gaps, and manages multiple enhancements. Collaborates with business and technical stakeholders, mentors analysts, leads continuous improvement, documents solutions, trains users, and develops automated and AI-enabled efficiencies.

Lead the design and development of scalable data pipelines, microservices, cloud-native ingestion systems, and streaming solutions. Collaborate with architects, engineers, product teams, and business stakeholders to build reliable data platforms using Python, Java, AWS services, SQL, and relational and NoSQL databases. Establish data quality, testing, monitoring, automation, and validation frameworks while optimizing performance and resolving production issues across distributed systems.

Lead analytics for small business insurance marketing by developing audience segments, targeting strategies, campaign measurement, and experimentation plans. Partner with marketing, insurance, analytics, and technology teams to align data definitions, build scalable data assets, diagnose analytical issues, and deliver executive-ready insights. Use Python, SQL, cloud platforms, and modern data warehousing to improve campaign performance, conversion, incrementality, and decision-making.

Lead design and implementation of data infrastructure, pipelines, and warehouses using Azure Data Factory, Databricks, and Snowflake. Manage data modeling, quality, security, and analytics frameworks. Lead client accounts, mentor teams, and drive innovation to deliver data-driven business insights.

Leads global customer experience analytics strategy, developing analytical frameworks, predictive models, KPI systems, dashboards, forecasting, segmentation, retention analysis, and operational insights. Partners with CX, contact center, product, data, and business leaders to improve satisfaction, efficiency, retention, and business outcomes. Manages analytics initiatives and teams while translating complex data into executive-level recommendations and narratives.

As a Pricing and Revenue Planner, you will manage pricing analytics, forecasting, synthesizing data into actionable insights, and collaborating with various teams to drive revenue improvements and profitability analysis.

Build and migrate production data engineering and machine learning workloads to Databricks on Azure. Assess on-premises SQL Server and SSIS systems, develop PySpark and Spark SQL pipelines, implement governance with Unity Catalog, and create reusable CI/CD assets. Work directly with Department of Veterans Affairs teams through requirements sessions, workshops, training, and documentation. Depending on focus, build ML workflows or modernize ETL, reporting, semantic models, and Power BI solutions.