Associate II - Data Analytics

Job Summary

The Data Analyst 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 Data Analyst 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.
• Perform descriptive, diagnostic, variance, trend, segmentation, and root-cause analyses to answer defined business questions.
• Develop analytical datasets, summaries, tables, charts, and other deliverables that support analytics products and decision-making.
• Use SQL, Python, and approved analytical tools to perform repeatable data preparation, analysis, validation, and automation.
• 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.
• Translate clarified requirements into defined data needs, calculations, mappings, business rules, and acceptance criteria.
• Profile source data to assess availability, structure, completeness, quality, limitations, and suitability for the intended use.
• Support the development and maintenance of analytical datasets, data models, and reusable data structures.
• 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.
• Develop and execute data-focused test scenarios, expected results, reconciliation procedures, defect documentation, and retesting.
• Investigate data discrepancies and support root-cause analysis and sustainable resolution.
• Document assumptions, known limitations, data-quality concerns, and unresolved issues.
• 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.
• Apply established practices for version control, peer review, testing, documentation, security, privacy, and data governance.
• Develop repeatable analytical methods, templates, and processes using approved tools and standards.
• Communicate work status, dependencies, risks, findings, and limitations clearly to technical and nontechnical partners.
• 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.
• Independently validate AI-assisted code, calculations, interpretations, and outputs against approved data and established requirements.
• Protect confidential and restricted information by following applicable privacy, security, governance, and responsible-AI requirements.
• Identify repetitive analytical tasks that may be standardized, automated, or accelerated through approved AI-enabled methods.

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.
• One or more years of relevant experience in data analysis, business intelligence, analytics, or a related discipline.
• Experience using SQL and Python to prepare, analyze, validate, and reconcile data; proficiency with Excel and working knowledge of data-modeling fundamentals.
• Experience working with cloud-based analytical platforms or distributed databases.
• Experience translating defined business questions or requirements into data needs, calculations, analytical outputs, and documented findings.
• Experience validating data, documenting assumptions and limitations, and handling confidential information in accordance with privacy, security, and governance requirements.

Must Have Skills

• Hands-on experience using SQL and Python for data preparation, analysis, validation, reconciliation, and automation.
• Proficiency with Excel and working knowledge of data-modeling fundamentals.
• Ability to prepare, combine, transform, profile, and analyze HR, workforce, operational, and related data from approved sources.
• Ability to perform descriptive, diagnostic, variance, trend, segmentation, and root-cause analysis.
• Ability to translate defined business questions and requirements into data needs, calculations, mappings, business rules, acceptance criteria, and analytical outputs.
• Strong data-quality, testing, reconciliation, troubleshooting, and documentation discipline.
• Ability to identify trends, anomalies, risks, and opportunities and communicate relevant findings and practical implications.
• Ability to communicate analytical findings, assumptions, limitations, work status, risks, and dependencies clearly to technical and nontechnical stakeholders.
• Foundational AI literacy and ability to responsibly use and independently validate outputs from approved AI tools.

Nice to Have Skills

• Experience with HR or workforce data and platforms such as Workday, Oracle HCM, PeopleSoft, or ServiceNow.
• Experience with BigQuery or another cloud analytical platform such as Snowflake, Databricks, Azure, or AWS.
• Experience in healthcare, shared services, HR transformation, global capability centers, or another regulated environment.
• Experience with statistical analysis, data visualization, survey analysis, product analytics, or process analytics.
• Experience creating reusable analysis templates, data dictionaries, metric documentation, or quality checklists.
• Experience using approved AI tools for data profiling, code assistance, documentation, quality checks, or analysis summaries.

Licenses, Certifications Training

• N/A required.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.