Senior Data Analyst

Berkshire Hathaway GUARD Insurance Companies•Scottsdale, AZ

5d•$77,763 - $180,082•Hybrid

About The Position

Good things are happening at Berkshire Hathaway GUARD Insurance Companies—an A+ (Superior) rated, nationwide Property & Casualty insurer backed by Berkshire Hathaway. With supportive leadership, collaborative teams, and opportunities to grow, GUARD is a place where people build meaningful, long‑term careers. The Senior Data Analyst serves as a strategic partner between business stakeholders, Data Product Managers, and technical teams to transform business requirements into trusted analytical solutions and data products. This role is responsible for requirements analysis, data validation, metric development, business intelligence solutions, and data governance activities that support operational, underwriting, claims, actuarial, and enterprise decision-making. The Sr Data Analyst leverages advanced analytical skills, deep insurance business knowledge, and data expertise to ensure data assets are accurate, accessible, and aligned with organizational objectives. The Senior Data Analyst plays a key role in supporting data-driven business decisions and advancing enterprise data product capabilities.

Requirements

3+ years of experience in product management, data and analytics, business analysis, consulting, strategy, or a related field within the insurance industry.
Demonstrated knowledge of underwriting, actuarial, rate/product, portfolio, or underwriting operations processes.
Experience identifying business problems, shaping opportunities, and delivering insurance data solutions that support underwriting decisions, pricing analysis, portfolio insight, or operational execution.
Strong business acumen and ability to connect data capabilities to underwriting, actuarial, product, pricing, portfolio, or operational outcomes.
Experience leading cross-functional teams through ambiguity, aligning stakeholders, and influencing without direct authority.
Experience facilitating workshops, interviews, discovery sessions, prioritization discussions, and product planning conversations.
Ability to translate between business needs and data or technical concepts to support shared understanding and sound decisions.
Ability to define product goals, success measures, roadmaps, adoption plans, and prioritization criteria.
Proven ability to adapt, solve problems, and make progress in a changing environment.
Excellent spoken and written communication.

Nice To Haves

5+ years in data analysis, business analysis, analytics, reporting, or a related data-focused role.
Strong Property & Casualty insurance knowledge, including business concepts, metrics, and processes across underwriting, claims, actuarial, or operations.
Advanced SQL skills, including data profiling, joins, aggregations, reconciliations, validation, issue investigation, and analysis across complex data sets.
Hands-on Power BI (or comparable) experience, including requirements, dashboard design, prototyping, semantic modeling, development, testing, deployment, and user enablement.
Experience translating business needs into requirements, metric definitions, quality expectations, BI solutions, and implementation-ready inputs for engineering teams.
Experience partnering with engineers, governance teams, QA analysts, and business stakeholders on modeling, source-to-target mapping, validation, and reusable consumption layers.
Familiarity with modern data concepts such as data products, data mesh, semantic models, metadata management, data quality, access controls, and governed consumption patterns.
Strong analytical, problem-solving, facilitation, and communication skills across business and technical audiences.
Ability to work through ambiguity, organize details, influence across teams, and support outcome-focused delivery.
Bachelor’s degree in data science, Business Analytics, Statistics, Mathematics, Computer Science, Information Systems, Business, Insurance, or a related field preferred.

Responsibilities

Partner with Data Product Managers, business leaders, and cross-functional stakeholders to clarify complex business problems, define requirements, establish success measures, and drive alignment on data product adoption and business outcomes.
Lead the translation of insurance business needs into implementation-ready requirements, including business rules, data definitions, source-to-target mappings, metric logic, acceptance criteria, and data quality standards.
Utilize advanced SQL techniques to profile data, validate business logic, investigate data anomalies, reconcile results, assess data quality, and provide actionable insights across complex and integrated data environments.
Own and lead the Power BI development lifecycle for assigned domains, including requirements gathering, solution design, dashboard development, validation, deployment, user adoption, and continuous improvement initiatives.
Lead descriptive and diagnostic analytics efforts to identify trends, root causes, opportunities, and risks, delivering actionable insights that support operational and strategic decision-making.
Collaborate closely with data engineering teams to define data models, semantic layer requirements, reusable dimensions, governed metrics, and scalable data structures that support enterprise analytics capabilities.
Lead business validation activities and data product quality reviews with Data Product Managers, QA analysts, and business stakeholders to ensure solutions are accurate, reliable, usable, and aligned with business needs.
Establish and maintain comprehensive documentation, including business definitions, metric definitions, data dictionaries, reporting standards, usage guidance, and key• decision records to support data governance and consistency.
Support the successful rollout and adoption of data products by communicating business value, facilitating stakeholder engagement, gathering user feedback, monitoring utilization, and identifying enhancement opportunities.
Serve as a subject matter resource for data analysis, reporting, and business intelligence best practices, providing guidance and knowledge sharing to analysts and business partners as appropriate.
Provide guidance, mentoring, and technical support to Data Analysts and other team members, fostering analytical capability and adherence to best practices.