Top Data & Analytics Jobs in Salt Lake City, UT

Develops scalable business intelligence and analytics solutions for financial, operational, clinical, and healthcare initiatives. Extracts, transforms, and analyzes data; builds ETL pipelines, reports, models, and applications; performs ad hoc analyses; improves data quality and architecture; and translates stakeholder needs into actionable insights. Collaborates with population health and cross-functional teams to support value-based care, provider outcomes, and strategic decision-making.

Leads the full digital experience research lifecycle, including user interviews, testing, surveys, behavioral analysis, experimentation, and conversion optimization. Defines research and web measurement strategies, analyzes qualitative and quantitative data, builds dashboards and executive presentations, and translates findings into compelling data stories. Partners with marketing, technology, design, product, and development teams to improve global web experiences, while maintaining scalable research documentation and insight repositories.

Develops and maintains Power BI dashboards and data models, defines KPIs with stakeholders, validates data accuracy, and collaborates with Data Engineering on optimized analytical models. The role documents data lineage and reporting assumptions, supports self-service BI through communications and training, and automates manual reporting processes. It requires SQL, Power BI, DAX, Power Query, data modeling, and data warehouse knowledge, plus strong communication and cross-functional collaboration skills.

Develops advanced operations research models, analytics, decision-support tools, and data solutions for transportation and network optimization. Responsibilities include collecting and analyzing data, evaluating systems and networks, identifying performance improvements, building predictive and prescriptive models, presenting recommendations to senior management, leading projects, and mentoring other operations research professionals.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Analyze financial, operational, commercial, customer, and organizational performance metrics. Build dashboards, scorecards, and executive reports; synthesize data into actionable insights; support forecasting, scenario analysis, business planning, strategic initiatives, and executive decision-making through quantitative analysis and modeling.

Design and implement enterprise data architectures supporting batch and real-time analytics, streaming pipelines, data warehouses, lakehouses, and generative AI applications. Build hands-on ETL/ELT solutions using Python and SQL, with Treasure Data/CDP, Kafka, Snowflake, Databricks, cloud services, CDC, and Debezium. Develop AI-ready pipelines, semantic search, RAG and vector-search infrastructure, and integrate enterprise data with modern AI platforms. This remote contract requires production-grade architecture experience rather than traditional ETL or BI maintenance alone.