Top Analysis Reporting Jobs in Salt Lake City, UT

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Develop customer analytics that improve retention, growth, adoption, and operational performance. Build and maintain BI dashboards, customer health metrics, churn-risk reporting, and predictive models. Partner with Client Success, Product, Sales, and Operations to translate complex findings into actionable recommendations. The role also owns data quality, governance, reporting automation, workflow improvements, and stakeholder communication.

Build and maintain secure data pipelines and integrations across business platforms, laboratory systems, analytical instruments, and messy data sources. Own schema design, orchestration, monitoring, quality controls, documentation, and compliant data isolation for export-controlled information. Create a unified lakehouse foundation supporting analytics, dashboards, machine learning, R&D, manufacturing, and operations. Partner with scientists and technical teams to integrate HPLC, GC, Invert Bio, APIs, spreadsheets, PDFs, and semi-structured records.

Support Maui post-disaster recovery by managing and developing project data systems, building backend database structures, updating website front-ends, and producing internal and external data metrics and visualizations to inform project leads, improve forecasting, and enable transparent, resilient infrastructure decision-making.

Develop and support enterprise BI solutions, including dashboards, reports, governed datasets, data marts, and ETL/ELT processes. Translate business requirements into technical designs, perform data analysis and validation, troubleshoot production issues, and maintain documentation, governance, security, and data-quality standards. Lead-level candidates will guide technical decisions, mentor team members, and coordinate complex initiatives and incidents.

Develop and maintain Power BI reports, dashboards, apps, and reusable semantic models using SQL, DAX, and Power Query. Own day-to-day Power BI platform operations, including workspaces, publishing, licensing, refreshes, gateways, permissions, and content lifecycle. Translate stakeholder requirements into decision-ready reporting experiences, validate data and calculations, support testing and releases, troubleshoot issues, and coordinate with engineering and security teams. Coach report authors and promote governed, reliable self-service analytics.

Build and maintain HR data pipelines and dbt models on GCP and BigQuery, integrating systems such as Workday and Greenhouse. Develop self-service dashboards, recruiting reports, data validation, governance, and AI-powered analytics experiences. Translate HR and recruiting data into actionable insights, improve reporting automation, and support stakeholders with decision-ready information while helping establish scalable analytics processes.

The Director of Data Engineering will lead a data engineering team and modernize the organization’s data infrastructure. Responsibilities include designing and managing data pipelines, presentation layers, ETL processes, migrations, automation, monitoring, and data quality practices. The role supports reporting, analytics, data science, governance, and business applications; collaborates with IT and stakeholders; evaluates new technologies; establishes engineering standards; and mentors team members.

Design and govern end-to-end data architecture for an integrated supply-chain e-commerce platform: create data models, build scalable ETL/ELT pipelines, ensure GDPR-compliant data governance, enable real-time analytics, optimize databases, plan retention/archival, and prepare data for LLM fine-tuning and RAG use cases.

Analyze and model property catastrophe reinsurance treaties using catastrophe modeling platforms. Prepare and validate exposure data, execute models, interpret risk metrics, develop scenarios, and identify portfolio risk drivers. Support underwriting, actuarial pricing, placement, renewals, and management decisions through clear reporting. Participate in model validation, calibration, research, and development while collaborating with underwriters, actuaries, and other stakeholders.