Analyst I - Data Analytics

Job Summary

The Senior Data Analyst performs complex analytical data work and owns major deliverables or defined workstreams that support HR analytics products and business decision-making. This role works with HR Analytics leaders, Business Analysts, subject matter experts, and technical partners to resolve ambiguity, translate requirements into technical data needs, business rules, calculations, and acceptance criteria, and determine appropriate analytical approaches. The Senior Data Analyst independently develops, validates, and documents analytical deliverables; identifies risks, dependencies, assumptions, and data limitations; and reviews outputs for accuracy, completeness, and fitness for use. The role requires advanced analytical and technical capability, independent judgment, and the ability to provide technical guidance and contribute to reusable methods, standards, documentation, and quality practices.

Responsibilities

Senior-Level Ownership Delivery Leadership

• Own the end-to-end delivery of higher-complexity analyses, analytical datasets, and defined data workstreams, from source assessment and technical design through development, validation, release, ongoing support, enhancement, and retirement where applicable.
• Lead major deliverables or defined workstreams with limited oversight, working with HR Analytics leaders, Business Analysts, subject matter experts, and technical partners to translate clarified business needs into scope, data requirements, success measures, technical outputs, delivery plans, and recommendations.
• Establish and manage priorities for assigned deliverables while balancing new development with data reliability, production support, reusable solution design, and reduction of technical debt.
• Identify delivery risks, data limitations, technical dependencies, requirement gaps, and alignment issues early and recommend practical mitigation actions.
• Review analytical deliverables before release for analytical validity, technical accuracy, completeness, data quality, traceability, governance alignment, fitness for use, and operational supportability.
• Provide technical guidance, peer review, and mentoring to less experienced analysts while reinforcing documentation, testing, validation, and development standards.

Data Analysis Analytical Delivery

• Extract, clean, transform, combine, and validate complex HR, workforce, operational, product, and transformation data from approved sources.
• Perform advanced descriptive, diagnostic, variance, trend, segmentation, root-cause, and other analyses to address complex business questions.
• Develop and validate complex analytical datasets, summaries, tables, charts, and recommendations that support analytics products and decision-making.
• Use advanced SQL, Python, and approved analytical tools to develop repeatable, efficient, maintainable, and well-tested analytical processes.
• Interpret complex results, distinguish supported conclusions from uncertainty, and explain findings, risks, limitations, and practical implications.

Data Requirements Solution Support

• Work with HR Analytics leaders, Business Analysts, subject matter experts, and technical partners to resolve ambiguity and refine complex analytical and data requirements.
• Translate clarified requirements into detailed data needs, calculations, mappings, business rules, acceptance criteria, and technical approaches.
• Assess unfamiliar or complex source data to determine its availability, structure, grain, quality, completeness, limitations, dependencies, and fitness for use.
• Develop and maintain major analytical datasets, data models, and reusable data structures within assigned workstreams.
• Partner with BI developers, data engineers, data quality teams, and other technical partners to resolve dependencies and ensure data outputs support approved analytical and reporting use cases.

Metric Support, Data Quality Validation

• Define and validate metric definitions, calculations, filters, cohorts, and business rules for complex analyses and assigned data workstreams.
• Establish and perform comprehensive data-quality, reconciliation, and validation procedures for complex deliverables.
• Plan and oversee data-focused testing, including test scenarios, expected results, reconciliation, defect documentation, retesting, and release validation.
• Investigate complex data discrepancies, identify root causes and downstream impacts, and implement or coordinate durable corrective actions with appropriate partners.
• Document material assumptions, data limitations, quality concerns, unresolved issues, and validation results.

Stakeholder Partnership Communication

• Partner with HR Analytics leaders, Business Analysts, subject matter experts, and technical partners throughout delivery to refine analytical questions, metric logic, acceptance criteria, and expected outputs.
• Communicate delivery status, recommendations, assumptions, risks, dependencies, data limitations, and timelines clearly to technical and nontechnical stakeholders.
• Present complex analytical findings in a clear, concise, and actionable manner without overstating what the data supports.

Documentation, Standards Operational Support

• Create and maintain comprehensive documentation for data sources, requirements, mappings, metric definitions, business rules, calculations, transformation logic, testing, dependencies, lineage, and known limitations.
• Apply and reinforce established practices for version control, peer review, modular development, testing, deployment, release management, documentation, security, privacy, and data governance.
• Develop reusable analytical methods, templates, standards, and quality practices that improve consistency, scalability, maintainability, and delivery efficiency.
• Support deployment, post-release validation, production issue investigation, and ongoing maintenance in collaboration with appropriate technical partners.
• Protect confidential HR and workforce information through approved access controls, data-handling practices, security requirements, and privacy and compliance standards.

AI-Assisted Analytics

• Use approved AI-enabled tools to accelerate complex data exploration, code development, documentation, data profiling, test development, quality review, and insight summarization.
• Critically evaluate and independently validate AI-assisted code, calculations, interpretations, and outputs against approved data, source documentation, and established requirements.
• Identify and address risks associated with AI-assisted analysis, including unsupported conclusions, bias, data exposure, inadequate testing, and insufficient business context.
• Develop or contribute to reusable and responsible AI-assisted analytical methods, documentation, controls, and quality practices.
• Follow responsible AI, privacy, security, and HR-data handling requirements, including restrictions on entering confidential or restricted information into unapproved tools.

Education Experience

• Bachelor's degree in Business, Human Resources, Information Systems, Computer Science, Data Analytics, Statistics, Engineering, or a related field; an equivalent combination of education and experience may be considered.
• Five or more years of relevant experience in data analysis, business intelligence, analytics engineering, or a related discipline.
• Advanced experience using SQL and Python to prepare, analyze, validate, and reconcile complex data; proficiency with Excel and working knowledge of data-modeling fundamentals.
• Experience working with cloud-based analytical platforms or distributed databases.

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