Top Analysis Reporting Jobs in Salt Lake City, UT

Serve as the Deposits, Liquidity, and Pricing & Rates data domain expert. Establish trusted data definitions, lineage, quality, governance, discoverability, and AI readiness. Partner with AI, engineering, product, and governance teams to support forecasting, funding, account visibility, and pricing use cases. Lead data issue resolution, platform integrations, cross-functional decision-making, and strategic initiatives while translating technical data concepts into business recommendations and ensuring risk and compliance requirements are met.

Build and maintain scalable fraud data pipelines, Snowflake data marts, dbt models, and summarized metric tables. Partner with fraud analysts and engineering teams to enable reporting, scorecards, monitoring, and operational decision-making. Establish data engineering standards, documentation, testing, governance, data quality, and pipeline reliability for audit-ready fraud analytics infrastructure.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Build and maintain ETL/ELT pipelines to move product data into analytics-ready stores (Postgres, data lake, warehouse). Design and optimize data models, ensure data quality and documentation, support ad-hoc research requests, and collaborate with research and engineering teams to enable reproducible analytics.

Owns marketing and go-to-market analytics, KPI definitions, attribution, campaign and sales-play measurement, scoring-model evaluation, dashboards, and weekly leadership reporting. The role reconciles Salesforce, HubSpot, and attribution data; partners with data teams on source-of-truth datasets; and builds AI-powered analytics agents, automated reporting, anomaly alerts, and data-quality processes to guide budget and pipeline decisions.

Analyze and optimize drug pricing using data-driven models, dashboards, reports, and strategic frameworks. Partner with Sales, Operations, Marketing, Finance, Product, pharmacy partners, and data teams to identify opportunities, define success metrics, improve reporting, and balance pharmacy economics with patient affordability. Serve as a trusted analytical advisor and support backend data development and query optimization.

Own analytical projects supporting revenue, profitability, and operational performance for the insurance business. Analyze funnels, campaigns, conversions, costs, and trends; build SQL queries and Looker dashboards; automate reporting with Python; define KPIs; validate data; and present actionable recommendations to stakeholders and leadership.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Design and implement customer data solutions using Adobe Experience Platform and RTCDP. Configure XDM schemas, datasets, identities, and real-time profiles. Build ingestion pipelines, audience segmentation, and activation strategies. Ensure data quality, governance, and optimize platform performance while collaborating with marketing and technical teams.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.