Data Analyst

• Lead, mentor, and develop a team of analysts, including performance management, coaching, and development planning.
• Define and execute the strategic vision for People Analytics, reporting, data governance, and data management within Human Resources.
• Deliver high quality dashboards, reports, and advanced analyses using statistical methods, modeling, and predictive techniques to generate actionable workforce insights.
• Lead the evolution of people analytics from descriptive reporting to diagnostic and predictive insights that inform workforce strategy and business outcomes.
• Operate as a strategic advisor to HR and business leaders by translating workforce challenges into analytics solutions and actionable recommendations.
• Partner with HR leadership and stakeholders to identify analytics priorities and align work to strategic business needs.
• Establish, monitor, and standardize key workforce metrics and KPIs to support executive decision making.
• Translate complex analytical outputs into clear, compelling insights and executive level presentations.
• Oversee data management practices, including data quality, integrity, accessibility, and lifecycle management.
• Supervise data security activities, including access controls, data handling practices, and adherence to privacy and protection requirements.
• Ensure compliance with organizational data governance, privacy, and security standards.
• Serve as a data steward and represent HR in governance councils, ensuring adherence to data management, security, and governance standards.
• Lead cross functional data initiatives, including negotiating data sharing and improving data accessibility.
• Promote data literacy by enabling leaders to effectively interpret, use, and apply analytics and reporting.
• Collaborate with global partners to support international reporting needs while ensuring compliance with data privacy and protection requirements.
• Partner with IT and security teams to ensure appropriate controls, risk mitigation, and alignment with enterprise data security policies.
• Champion continuous improvement in analytics tools, processes, and capabilities.

HR Data and Analytics Manager

• Initiating the development and deployment of data analytics at Intermountain Health.
• Acting in a more senior capacity, contributing to strategic initiatives, clinical standards, and operational goals through data insights.
• Collaborating with business and clinical leaders across Intermountain Health.
• Providing advanced support for management level leadership in a business or clinical area.
• Developing a deep understanding of customer data and business/clinical operations they support.
• Fulfilling complex customer requests through data exploration, report development, and visual storytelling.
• Communicating analytic findings to their customers and providing project updates.
• Leading analytic work demonstrations and hands-on training for customers.
• Staying updated on industry trends and advancements.

Senior Data Analyst

• Partner directly with stakeholders across the business (Product Development, Marketing, DTC, B2B, Finance, Operations, Inventory Planning) to translate ambiguous questions into well-defined analyses, dashboards, and data products. You’ll own these end-to-end: scoping, building, validating, and communicating findings.
• Build and maintain dbt models that turn raw source-system data into trustworthy, well-documented datasets. Write the tests and documentation that let both humans and AI agents downstream rely on the work.
• Develop and maintain the semantic context, dashboards, and reports that the rest of the business uses to operate day-to-day.
• Own metric definitions and business semantics. Drive alignment when stakeholders disagree on what a definition or number means.
• Review and harden AI-generated SQL, dbt models, and Python code with the judgment to catch issues that pass tests but are semantically wrong. The majority of your output will be code you’ve collaborated on with AI agents, and you’ll bring the data intuition that makes that work trustworthy.
• Investigate ambiguous data questions where the answer isn’t in the schema: talk to source-system owners, investigate edge cases, reconcile conflicting definitions, and improve our model of the business.
• Help build and maintain Stio’s data infrastructure — currently Snowflake, Fivetran, dbt, GitHub, Power BI, R, and Python — and contribute to decisions about where the stack should evolve.
• Improve data governance for both the Data & Analytics team and the business at large by creating documentation that’s actually useful and that AI agents can consume as context for future work.
• Continuously develop your skills as the practice of data analytics evolves. This is a real part of the job, not something done on the side.

• Maintain the KPI standards for Customer Support: document how each metric is calculated and interpreted, keep definitions current across data platforms, and flag to leadership when targets may need adjustment.
• Build and maintain dashboards in Omni Analytics (our BI platform) that serve analyst, manager, and leadership audiences; maintain the semantic layer and metric definitions so every team measures the same things the same way.
• Produce formal analyses and recommendations on volume trends, resolution efficiency, CSAT, contact rate, and cost performance; frame findings in terms of business impact, not just numbers.
• Identify anomalies and performance gaps in Support data; bring a recommended course of action to leadership rather than a flag, and track the response through to resolution.
• Consult on SQL, Snowflake, and BI standards used for Support reporting; ensure Support modeling approach, query patterns, and tooling match organizational standards.
• Lead metric alignment conversations with Data, Product, and Engineering; represent Support's analytical interests and keep shared definitions from drifting.
• Triage and scope ad hoc analysis requests from across the organization; exercise judgment on urgency, feasibility, and which questions are worth answering.

• Sit with internal stakeholders (Medicare sales, finance, operations, HR) to walk them through Omni dashboards, capture feedback, document defects and change requests, and track them to resolution.
• Make report-side changes in Omni: tile edits, formatting, labels, filters, and layout. Escalate anything that touches dbt models or schema to the Interim Head of Analytics.
• Write SQL to spot-check numbers, audit data quality, and answer one-off questions from the business.
• Maintain and update the entity-relationship documentation for the Mart layer as the schema evolves.
• Reconcile reported numbers against source systems (Sabal, Atlantis, Paylocity, Sonar, Retreaver) when discrepancies are flagged.
• Own a clean, organized backlog of business requests and known issues so nothing falls through the cracks.

Junior Data Analyst

• Analyze people data to identify trends, insights, and opportunities.
• Use creative problem-solving to inform business strategy and HR initiatives.
• Create and maintain interactive dashboards using Power BI to present data insights clearly and effectively.
• Partner with HR, Talent Acquisition, and other business units to understand data needs and provide actionable insights.
• Outline, document, and refine processes related to data analytics, including data sharing, usage, and related use cases.
• Support HR projects such as Employee Listening, Performance Management, and Workforce Planning by providing data-driven insights and analysis.
• Implement improvements to data architecture and ensure compliance with internal policies and external regulations regarding data collection and sharing.
• Lead training sessions for users on best practices for utilizing people data and data visualization tools to enhance data-driven decision-making.
• Manage ad-hoc data requests from various stakeholders and develop self-service solutions to improve efficiency and accessibility.

People Data Analyst

• Provide department leadership with reliable data from trusted source systems that will empower and influence key decision making.
• Access and integrate data for reporting to establish data standards, quality, and reliability.
• Apply advance analytics, foundational data science techniques, and responsible for generating insights and enhanced data-driven storytelling – while maintaining data quality and data governance.
• Document data definitions, lineage, and relationships across multiple systems.
• Identify opportunities to apply basic data science techniques (e.g., clustering, trend analysis, text analysis) to improve understanding of business data.
• Validate data quality including sources, reliability, completeness, validity, and uniqueness.
• Use AI‑assisted profiling, statistical methods, and anomaly detection to uncover data issues.
• Design and maintain data models that support analytics, dashboards, and entry‑level data science use cases.
• Prepare datasets for exploration, feature analysis, and predictive modeling through effective structuring and aggregation.
• Build queries, datasets, and feature-ready tables using SQL and Databricks.
• Translate business questions into dashboards, reports, and analytical summaries that are actionable and measurable.

Data Analyst 3 – Finance and Records

• Lead end-to-end design and technical delivery of complex AI/BI architecture initiatives aligned with enterprise strategy and roadmaps.
• Provide technical leadership across programs ensuring robust design principles, effective tools, risk management, and delivery excellence.
• Approve and govern solution architecture for AI/Data products to ensure alignment with enterprise AI/analytics standards and industry best practices.
• Translate business requirements into scalable AI/analytics solutions, selecting appropriate technologies and approaches.
• Design and validate secure, reliable, high-performing analytics architectures that meet business needs and service expectations.
• Develop AI & analytics data models (e.g., AI agents, data marts) and ensure effective data pipelines for transformation, quality, and loading.
• Support solution implementation and operations through documentation, training, stakeholder communication, and issue resolution.

Specialist I – TIS AI and Data Analytics

• Work with clients and peers to build a high performing system using Elastic to aggregate logs from many systems into a single common schema
• Use Elastic Common Schema (ECS) formatted fields, create quality visualizations and alerts that analyst can use for threat hunting
• Maintain infrastructure, and identify problems or anomalous behavior before they become a larger issue and can be actioned on
• Work with the vendor to determine best practices for deployment and maintenance of system architecture and deploy within designated security requirements

Data Analytics Engineer

• Own and drive weekly sales reporting and performance analysis across multiple systems; synthesize insights into clear, actionable recommendations for sales leadership and cross-functional teams
• Leverage complex datasets to identify growth opportunities, performance drivers, and risks, translating findings into strategic actions
• Lead short-term and long-range sales forecasting for retail POS, incorporating variables such as new item launches, distribution changes, merchandising programs, seasonality, and promotions
• Own the monthly customer facing reforecasting process, proactively communicating updates, risks, and opportunities
• Act as a strategic partner to Demand Planning to ensure forecast alignment, accuracy, and operational feasibility
• Lead weekly business reviews, aligning teams on performance, tracking progress against priorities, and driving decision-making
• Drive cross-functional alignment across Sales, Supply Chain, and Marketing to ensure execution against business objectives
• Identify process gaps and inefficiencies, and develop scalable solutions to improve reporting, forecasting, and planning workflows
• Monitor competitive landscape, including innovation, pricing, and market trends, to inform strategic recommendations
• Support and influence key sales deliverables, including retailer presentations, merchandising strategies, and critical business reviews
• Own audit responses and data validation, ensuring accuracy, consistency, and compliance across deliverables
• Maintain and evolve critical business tools and materials, ensuring accessibility, accuracy, and usability for internal and external stakeholders

Sales Strategy & Analytics Manager

• Conduct in-depth analysis of transactional and customer data to identify unusual patterns and potentially fraudulent activities
• Participate in discussions with business partners to define the CB fraud risk appetite
• Participate in periodic fraud risk assessments for CB products and services
• Assist in developing, implementing, refining, monitoring and providing performance tracking of CB fraud strategies to minimize CB fraud risk
• Collaborate with business partners on new CB product or service approval
• Assess and monitor the extent to which fraud strategies adversely impact customer experience
• Develop new fraud strategies to mitigate threats posed by emerging fraud trends in the industry
• Collect, process, and manage large sets of data related to customer transactions and interactions
• Provide reporting on fraud analytics performance, such as fraud KPI and/or KRI reporting or SLA reporting, to senior management
• Work closely with the IT and security teams to implement effective data analysis tools and systems
• Proactively research and identify areas of opportunity for improvement, working closely with the Credit and Fraud Operations team to identify recent fraud patterns, trends, and rings
• Ensure all activities adhere to legal and regulatory requirements as well as internal risk management requirements related to fraud and data privacy
• Complete required fraud strategy documentation and meet audit standards

AVP, Fraud Strategy Data Analytics – Deposits

• Proactive, technically-grounded analysis of sales motion and revenue operations.
• Building strong cross-functional relationships to drive alignment on key business objectives.
• Translating business requests into rigorous, reproducible analysis using AI tools to enhance workflow.
• Delivering findings to both technical and non-technical audiences.

Data Analyst – Revenue Ops

• Develop service specific knowledge through greater exposure to peers, internal experts, clients, regular self-study, and formal training opportunities.
• Participate in the collection and analysis of data using a variety of data tools.
• Support the building and implementing of solutions for external clients and internal projects.
• Working under the guidance of Data Analytics team leaders to develop custom data models and algorithms to apply to data sets.
• Develop predictive analytics, machine learning, and artificial intelligence techniques.
• Work individually as well as in collaboration with others.
• Take on additional roles beyond technical development and client service.

• Initiating the development and deployment of data analytics in support of Intermountain Health’s key strategy of Expanding Proactive Care.
• Collaborating with business and clinical leaders across Intermountain Health to effectively forecast financial performance under alternative payment models.
• Evaluating alternative payment model contracts with internal and external payers to determine performance viability in an integrated system.
• Performing make-good analyses on existing contracts and making recommendations for renewal.
• Developing a deep understanding of customer data and business/clinical operations they support.
• Preparing complex and in-depth analysis to identify financial feasibility, trends, and risks.
• Establishing reporting, KPIs, and performance monitoring for key proactive care strategies.
• Leading analytic work demonstrations and hands-on training for customers to promote understanding and independent consumption of analytic work.

Senior Data Analyst - Technical

• Contribute to the development and application of AI-driven solutions across our consulting work, with a focus on data analytics, coding, and scalable data pipelines.
• Work with large healthcare datasets, building and refining analytical models, and supporting the creation of clear, actionable visualizations.
• Apply and expand your experience in AI and data science while gaining exposure to actuarial concepts within a consulting environment.

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