Associate II - Data Analytics

Job Summary

The Associate II - Data Analytics supports HR Transformation Analytics by preparing, analyzing, validating, and documenting HR and workforce data used in analytics products and business decision-making. This role works with HR Analytics leaders, Business Analysts, subject matter experts, and technical partners to clarify requirements and translate them into defined data needs, business rules, calculations, and acceptance criteria.

The Associate II - Data Analytics delivers assigned analytical data components and analyses, applies disciplined practices for data quality, testing, reconciliation, and documentation, and communicates findings, assumptions, and limitations clearly. The role requires hands-on analytical capability, attention to detail, sound judgment, and the ability to work effectively within established methods and technical standards.

Responsibilities

• Data Analysis and Analytical Delivery Prepare, combine, transform, and analyze HR, workforce, operational, and related data from approved sources.
• Data Analysis and Analytical Delivery Perform descriptive, diagnostic, variance, trend, segmentation, and root-cause analyses to answer defined business questions.
• Data Analysis and Analytical Delivery Develop analytical datasets, summaries, tables, charts, and other deliverables that support analytics products and decision-making.
• Data Analysis and Analytical Delivery Use SQL, Python, and approved analytical tools to perform repeatable data preparation, analysis, validation, and automation.
• Data Analysis and Analytical Delivery Identify trends, anomalies, risks, and opportunities and communicate relevant findings and practical implications.
• Data Requirements and Solution Support Work with HR Analytics leaders, Business Analysts, subject matter experts, and technical partners to clarify assigned analytical and data requirements.
• Data Requirements and Solution Support Translate clarified requirements into defined data needs, calculations, mappings, business rules, and acceptance criteria.
• Data Requirements and Solution Support Profile source data to assess availability, structure, completeness, quality, limitations, and suitability for the intended use.
• Data Requirements and Solution Support Support the development and maintenance of analytical datasets, data models, and reusable data structures.
• Data Requirements and Solution Support Partner with BI developers and other technical teams to ensure data outputs support reporting, dashboards, semantic models, and analytical use cases.
• Data Quality, Testing, and Validation Perform data-quality checks and reconcile results across sources, calculations, and analytical outputs.
• Data Quality, Testing, and Validation Develop and execute data-focused test scenarios, expected results, reconciliation procedures, defect documentation, and retesting.
• Data Quality, Testing, and Validation Investigate data discrepancies and support root-cause analysis and sustainable resolution.
• Data Quality, Testing, and Validation Document assumptions, known limitations, data-quality concerns, and unresolved issues.
• Data Quality, Testing, and Validation Support deployment validation and investigation of production data issues with appropriate technical partners.
• Documentation, Standards, and Operational Support Maintain documentation for data sources, requirements, mappings, business rules, calculations, transformation logic, testing, and known limitations.
• Documentation, Standards, and Operational Support Apply established practices for version control, peer review, testing, documentation, security, privacy, and data governance.
• Documentation, Standards, and Operational Support Develop repeatable analytical methods, templates, and processes using approved tools and standards.
• Documentation, Standards, and Operational Support Communicate work status, dependencies, risks, findings, and limitations clearly to technical and nontechnical partners.
• Documentation, Standards, and Operational Support Manage assigned deliverables and priorities while escalating issues that require additional direction or technical support.
• AI-Assisted Analytics Use approved AI tools to support data exploration, code drafting, documentation, data profiling, quality checks, and insight summarization.
• AI-Assisted Analytics Independently validate AI-assisted code, calculations, interpretations, and outputs against approved data and established requirements.
• AI-Assisted Analytics Protect confidential and restricted information by following applicable privacy, security, governance, and responsible-AI requirements.
• AI-Assisted Analytics Identify repetitive analytical tasks that may be standardized, automated, or accelerated through approved AI-enabled methods.

Education Experience

• Education 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.

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