Top Data & Analytics Jobs in Salt Lake City, UT

Lead complex data analyses, develop statistical models, write advanced SQL, build data models and dashboards, and establish technical standards for analytics. Mentor analysts, troubleshoot data quality and modeling challenges, partner with data engineering, translate ambiguous business questions into rigorous analyses, and communicate findings to senior stakeholders. Own the analytics function’s technical roadmap while ensuring data privacy and security practices.

Build and maintain Engine X performance dashboards, reporting frameworks, data models, and complex SQL analyses using Snowflake. Track card, booking, rewards, spending, and retention metrics; investigate anomalies; support A/B tests; document metric definitions; and communicate findings to product, finance, and leadership. Collaborate with analytics and data engineering teams to validate pipelines and improve data quality.

Own product analytics and customer success data domains by building governed models for product usage, feature adoption, customer health, onboarding, renewals, churn, expansion, and net revenue retention. Partner with Product, Customer Success, RevOps, Finance, and analysts to create semantic models, metric definitions, instrumentation, documentation, alerting, and self-service tools. Use dbt, Snowflake, Omni, Hex, and AI-assisted development to turn ambiguous stakeholder questions into durable data products.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Own actuarial models and client-facing reporting measuring cost savings, outcomes, and performance of healthcare prior authorization programs. Analyze authorization and claims data, conduct impact evaluations, reconcile projected and realized results, investigate trends, and present executive-level insights to customers and leadership. Partner with clinical, customer success, data, and actuarial teams while maintaining reproducible, auditable methodology and documentation.

Build and support scalable data platforms and pipelines, leading migrations such as Snowflake to BigQuery and transitioning reporting to Looker. Responsibilities include data architecture, ETL/ELT, API and marketing integrations, data quality, production troubleshooting, warehousing, performance optimization, and platform modernization. The role partners with analytics and business teams, owns projects through production, documents solutions, and provides technical guidance.

Leads Data Engineering, Data Governance, and BI/Analytics teams while owning enterprise data platform architecture, pipelines, governance, warehouse or lakehouse design, BI strategy, and data quality. Serves as the technical bridge between data teams and business stakeholders, manages the platform roadmap, and oversees governed data integration for Glean AI search and agent tools. This hands-on director role combines architecture, engineering, people leadership, strategic planning, and stakeholder partnership.

Analyze and transform cloud data using SQL, dbt, and Google Cloud; build automated pipelines, data visualizations, validation checks, unit tests, monitoring, and alerts. Partner with engineering teams and enterprise insurance clients to improve data quality, governance, automation, and decision-making. Communicate insights clearly, document technical work, develop insurance domain expertise, and contribute to collaborative reviews and on-call support.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.