Top Senior Analysis Reporting Jobs in Salt Lake City, UT

Build and lead Surescripts’ commercial analytics function, including governed Salesforce and CRM data models, KPI definitions, dashboards, forecasting, pipeline analytics, customer journey insights, automated reporting, quota modeling, and territory planning. Manage an analyst and partner with Sales, Marketing, Finance, Data, and Business Applications. Present actionable recommendations to senior leaders while improving data quality and enabling self-service analytics.

Leads end-to-end business intelligence delivery, including requirements discovery, dimensional modeling, SQL development, Power BI dashboards and semantic models, testing, deployment, documentation, adoption, and support. Establishes BI standards, optimizes performance, validates reporting data, partners with stakeholders, communicates analytical insights, and mentors analysts and developers. Requires advanced Power BI and SQL expertise plus practical Microsoft Fabric knowledge.

Designs, builds, maintains, and optimizes scalable data pipelines and ETL/ELT workflows using Azure Databricks, SAS, SSIS, SQL, Python, and Spark. Integrates data from internal and external sources, improves performance and reliability, troubleshoots complex processing issues, and supports cloud-based data platform architecture. Collaborates with engineering, analytics, product management, SRE, and other stakeholders to deliver high-quality data solutions.

Lead consumer insights projects from brief development through research design, quantitative and qualitative analysis, storytelling, reporting, and client presentation. Manage client relationships, develop actionable recommendations, create surveys, oversee project plans, and coach junior team members. Serve as a strategic advisor by translating consumer and market data into business decisions.

Develop and support a scalable business intelligence and reporting platform using ClickHouse, Python ETL/ELT pipelines, advanced SQL, dimensional models, and reporting marts. Integrate data from Elasticsearch, SQL Server, APIs, and files; optimize analytical queries; build dashboards and reporting experiences; collaborate with business stakeholders; and develop AI-enabled features for natural-language querying, insights, anomaly investigation, and automated reporting intelligence.

Leads enterprise Workforce Intelligence across Customer Excellence, integrating workforce, operational, quality, customer, and service data into actionable insights. Establishes performance frameworks, executive reporting, governance, root-cause analysis, and reporting automation. Partners with Operations, Workforce Management, Finance, HR, Training, and Technology while leading a global team and driving performance improvements.

Build trusted GTM business intelligence by defining commercial metrics, analyzing performance across Sales, Marketing, Customer Success, Product, and Finance, diagnosing root causes, and delivering executive recommendations. Partner with data engineering and operations teams to integrate Salesforce, product, support, marketing, billing, and financial data. Evaluate AI-powered analytics capabilities and improve self-service decision-making.

Design, build, optimize, and operate enterprise cloud data platforms and Databricks ETL/ELT pipelines. Develop batch and streaming pipelines, data models, infrastructure-as-code solutions, BI dashboards, monitoring, and scalable ML models. Ensure platform reliability, governance, security, and compliance while collaborating with software, systems, product, and business teams.

Maintains enterprise data quality and governance by validating sources, managing metadata and data dictionaries, reconciling metrics, performing profiling and cleansing, documenting lineage, and supporting cross-system harmonization. Collaborates with reporting teams, dashboard developers, data owners, and PMO leadership to ensure reliable datasets, standardized definitions, accurate dashboards, and compliance with federal data governance and privacy requirements.

Own end-to-end production data platforms, writing Python and SQL, designing AWS pipelines, modeling analytics data, and implementing quality and reconciliation controls. Make architecture decisions independently, ensure correctness and auditability, and use AI coding tools effectively. The role also supports applied AI initiatives including entity resolution, LLM-assisted analytics, embeddings, and anomaly scoring.