Principal Data Analyst

The Principal Data Analyst is a senior, hands-on analytics leader who turns complex, cross‑functional data into clear, actionable insights that drive business outcomes as a consultative partner. This role sets the bar for analytical rigor, designs enterprise‑grade BI assets, mentors analysts, and partners with business, engineering, and governance to improve decision quality and operational performance. Work emphasizes advanced analytics using Databricks, SQL, R, Power BI, Python, and the Azure data stack.    Salary range: $100,003-120,000 plus 10% bonus   Remote hires will be required to travel to our headquarters in Ankeny, IA (company paid) on their first day for orientation.   At this time, we are NOT considering applicants that require immigration sponsorship (additional work authorization or permanent work authorization) now or in the future to work in the United States. This includes, but IS NOT LIMITED TO: F1-OPT, F1-CPT, H-1B, TN, L-1, J-1, etc.   Benefits Our employees enjoy a generous package of benefits that we are thrilled to provide, and feel is part of what makes us different as an employer. We value our team members, and this is one way we can show it.   Benefits include: -PTO, holiday pay and holiday of choice -401(k) match -Life insurance -Short-term disability -Health, dental and vision insurance -Maternity/paternity leave -Health savings account (HSA) -Flex spending accounts (FSA) – health and dependent

Position Responsibilities may include, but not limited to

• Lead high‑impact analytics initiatives from problem framing through delivery; quantify value, design robust analyses, and communicate recommendations to business partners and executives
• Partner with business leaders to identify analytics opportunities and deliver actionable insights. Present findings and recommendations to partners and executive leadership in clear, compelling formats
• Develop analytics products and solutions using Databricks, modern Business and Artificial Intelligence tools and Agile principles
• Implement advanced analytics where applicable, leveraging Python for advanced analytics, automation, and integration with Databricks workflows
• Architect and publish trusted Power BI datasets and dashboards (DAX, Power Query, semantic models), establishing standards for usability and adoption
• Build performant Databricks workflows (notebooks, jobs, SQL Warehouses) to wrangle large datasets, engineer features, and automate recurring analyses
• Develop Python scripts for automation, data wrangling, and integration with Databricks and Azure services
• Partner with Data Engineering on Azure data stack components (Data Factory, ADLS, Synapse/Fabric pipelines) for scalable data solutions
• Own analysis quality: data validation, experiment/study design, sensitivity checks, and reproducibility (versioning, documentation)
• Define and monitor KPIs; create executive scorecards and operational reporting that tie directly to business objectives
• Coach/mentor analysts and BI developers; uplift storytelling, statistical thinking, and visualization craftsmanship across the team
• Collaborate with Data Governance and Compliance to ensure appropriate use of PHI and adherence to privacy/security controls
• Facilitate data stewardship and literacy across the business
• Drive adoption of data products to scale data value
• Stay current with emerging technologies and recommend enhancements to the analytics stack

Required Skills and Experience

• Bachelor’s degree in Data Science, Computer Science, Statistics, Economics or related field
• 8–10+ years in analytics roles, including 3+ years leading projects
• Proven impact delivering analytics in complex environments (multi‑source data, ambiguous scope) with measurable business outcomes
• Experience operating in healthcare data environments (or demonstrated ability to quickly adapt to such contexts)
• Demonstrated ability to mentor junior analytics professionals
• Statistical and financial analytics expertise with deep understanding of appropriate study design and analysis techniques and business case development
• Azure Data Stack: Data Factory, ADLS, Synapse/Fabric pipelines; familiarity with security and governance features
• Databricks: Spark/SQL, notebooks & jobs, performance tuning, SQL Warehouses
• SQL: Strong proficiency (ANSI/T‑SQL); query optimization over large, partitioned tables
• R: Applied analytics using tidyverse/ggplot2; reproducible workflows; statistical testing and modeling fundamentals
• Python: Data wrangling, automation, integration with Databricks