Top Analysis Reporting Jobs in Salt Lake City, UT

Design, build, and optimize ETL pipelines and data platform architecture using Airflow, BigQuery, Python, and Spark. Lead technical decisions, mentor engineers, collaborate with product and stakeholders, and improve data-quality and testing frameworks to support data-driven products.

Build and maintain scalable cloud data pipelines and governance to support analytics across claims, EHR, and real-time patient data. Deploy and operate Snowflake/dbt/AWS-based infrastructure, enable real-time analytics, partner with clinical, product, and engineering teams, and deliver insights that inform care delivery, payer collaboration, and business decisions.

Lead cross-channel customer experience analysis by combining quantitative and qualitative signals (NPS/CSAT/CES, contact center, digital analytics, transactional and complaint data). Design and run deep-dive investigations, translate findings into operational recommendations, coach analysts, partner with channel owners, and maintain measurement and narrative standards to drive quarter-over-quarter operational change.

Lead enterprise customer segmentation and personalization analytics: design and run segmentations, interpret results, recommend activation strategies, partner with marketing and audience owners, track campaign performance, and coach analysts to maintain segmentation standards across the activation lifecycle.

Perform actuarial analytics and forecasting to model financial and market trends, support value-based care contracting and implementation, ensure data integrity, develop metrics and forecasts, troubleshoot data issues, and advise leadership to inform strategic and operational decisions.

Lead complex data, analytics, AI, and cloud transformation engagements as a trusted advisor: manage delivery, scope, budget, stakeholders, and teams; design scalable data platform solutions; drive business development, mentoring, and practice improvements.

Own end-to-end data pipelines (ingest, dbt transformations, BigQuery warehousing) and consolidate the analytics stack into a single source of truth. Build and maintain dbt models with tests and documentation, establish data quality and observability, own product analytics and instrumentation, partner with GTM and marketing on reporting, and prepare for high-volume/streaming ingestion.

Lead architecture and technical strategy for enterprise-scale cloud data platforms. Design scalable ETL/ELT pipelines and data models, drive governance, implement AI-assisted engineering tools, mentor senior engineers, and ensure secure, performant, and maintainable data solutions.

Lead data-driven contact strategy for delinquent customer outreach. Design and run Champion/Challenger tests across email, phone, and digital channels, build segmented outreach schedules, measure channel mix performance, and deliver a roadmap and executive-facing insights to optimize recovery, customer experience, and operational cost tradeoffs.

Design, build, test, and maintain scalable cloud-based data platforms and ETL/ELT pipelines (batch and near-real-time). Implement data models, data marts, governance, metadata, CI/CD and IaC, optimize pipeline performance, support migrations/DR, produce documentation, and collaborate with architects, DBAs, and stakeholders to meet federal security and audit requirements.

Lead design, build, test, and maintain scalable cloud data platforms and pipelines (ETL/ELT, batch and streaming). Ensure data quality, governance, security, CI/CD deployment, performance tuning, migration/archival, and mentor data engineering teams to support analytics and AI/ML use cases for a federal modernization program.

Lead design and execution of data migration, archival, and disaster recovery for a cloud-based court case management modernization. Own migration strategy, data lifecycle and retention, DR/HA design and drills, tooling evaluation, documentation, and coordination with Data Engineering, Governance, Security, and stakeholders to ensure auditable, resilient, and compliant migrations.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.