Top Business Intelligence Jobs in Dallas, TX

Lead data strategy and analytics initiatives, design data models and forecasts, perform advanced statistical analyses, produce executive reports, and mentor junior analysts. Manage cross-functional teams to deliver data-driven insights and improve data collection and analysis processes.

Lead design and ownership of ZoomInfo's PLG measurement system, define KPIs and leading indicators, analyze end-to-end customer journeys, diagnose growth issues, and influence product investments. Build AI-native, automated analytics workflows, reporting, and monitoring while partnering cross-functionally to prioritize experiments and scale insights that drive adoption, retention, monetization, and expansion.

Lead analytics and BI for Digital Product Operations: define product and adoption metrics, build dashboards, ensure data governance and quality, measure ProdOps effectiveness, coach PM/POs, diagnose adoption gaps, and drive interventions that improve product planning and delivery.

Lead Tilt's Business Intelligence function end-to-end: manage a distributed BI team, own reporting data models and semantic layer, build and maintain a metric-and-metadata library with reference SQL, review dbt work and hold quality through code review, prioritize stakeholder requests, and grow team technical and business fluency.

Own and maintain KPI reporting pipelines and business logic, ensure data quality, diagnose KPI changes, perform deep-dive analyses, build dashboards and visualizations, architect data models and curated reporting zones, and partner with Data Engineering and product/marketing stakeholders to enable self-service analytics.

Lead business intelligence efforts to analyze complex data, define KPIs, identify operational inefficiencies, and recommend process improvements. Collaborate with cross-functional teams and executives to implement data-driven strategies, manage improvement projects, monitor resource allocation, and ensure compliance with policies and regulations.

Lead product analytics efforts to define KPIs, build measurement frameworks, analyze customer behavior, and generate insights that influence product and engineering decisions. Partner with Product, Engineering, and Data teams to design event tracking, run experiments, quantify opportunity sizes, and communicate recommendations through clear narratives and visualizations to drive product adoption, retention, and business outcomes.

Lead ownership of BI reporting infrastructure: build and standardize dashboards, extract insights from messy third‑party data, partner with marketing teams to drive performance optimization and deliver proactive retrospective analyses that influence business decisions.

Lead BI developer owning end-to-end reporting for performance marketing verticals. Build and standardize dashboards, author LookML models, extract insights from messy third-party data, partner with Yield/Paid Media/Account teams, deliver retrospective analyses, and drive adoption and business impact.

Lead BI Developer owns reporting infrastructure end-to-end, building standardized, trusted dashboards and LookML models, analyzing messy third-party data, partnering with Yield/Paid Media/Account teams to deliver actionable performance marketing insights and retrospective analyses that drive business decisions.

Lead the architecture and development of CareDx's enterprise BI ecosystem. Own Power BI modeling, DAX optimization, governance (RLS, deployment pipelines), and self-service enablement. Collaborate with data engineering to build scalable Databricks/Delta Lake/PySpark pipelines, leverage Microsoft Fabric and Lakehouse architecture, mentor BI developers, translate business needs into analytics solutions, and drive BI performance and reliability.

The Business Intelligence Analyst analyzes complex data for finance teams, develops reports and dashboards, and enhances financial reporting solutions. They collaborate with teams to ensure reporting accuracy and alignment with business needs.

Lead a BI team to build and maintain dashboards, reports, and data models; enable self-service via semantic layers; partner with product, engineering, and stakeholders; drive AI and advanced analytics adoption; ensure data quality, pipeline clarity, and BI system reliability; prioritize backlog, mentor team members, and promote data-driven decision making.