Top Senior Analysis Reporting Jobs in Provo, UT

As a Senior Analytics Engineer, you'll build data pipelines and dashboards, partner with stakeholders for KPI development, and ensure data quality across the company.

The role involves acting as a subject matter expert in Data and AI, providing technical expertise during sales processes for Azure-based solutions, and mentoring team members to enhance client experiences.

The Director of Data Analytics & Business Intelligence leads analytics functions, building models and dashboards while guiding a team. Responsibilities include analytics infrastructure management, cross-functional collaboration, and driving AI/ML initiatives.

Lead legacy-to-SaaS data migrations: design and execute ETL, extract/transform/load data, validate integrity, map business workflows to target models, engage clients, document processes, support governance, and train stakeholders to ensure accurate, secure conversions.

Lead development of scalable enterprise analytics and data stewardship. Perform advanced data analysis, assess data quality and lineage, define cleansing strategies, manage metadata and business glossary, support enterprise data warehouse and analytic product backlogs, and collaborate with cross-functional teams to enable self-service and advanced analytics.

Design, build, and maintain Scala-based ETL and streaming data pipelines, APIs, and data microservices in Azure. Optimize LLM fine-tuning and RAG workflows, support DevOps/CI-CD practices, document standards, mentor engineers, and manage data ingestion frameworks for analytics and partner integrations.

Lead advanced analytics and informatics work to develop predictive models, optimize programs, and build financial/operational models. Translate complex data into actionable insights for stakeholders and senior leadership, influence departmental strategy, and independently design analytical approaches to improve consumer/member experiences and drive measurable business outcomes.

Partner with Product and Engineering to turn product data into insights: define AI/agent metrics, design and analyze experiments, build dashboards and reports, perform cohort and behavioral analysis, surface opportunities, and guide product decisions with measurement and instrumentation improvements.

Lead development and ownership of end-to-end data pipelines and platform tools. Build and optimize ingestion, transform data with dbt and Jinja, implement monitoring/alerting, manage deployments and CI/CD, and create self-service analytics applications and internal tooling.

Partner with Product, Design, Engineering, and Data to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and influence product analytics standards and long-term strategy.