Associate II - Data Analytics

Darwinbox Digital Solutions Pvt. Ltd.; India; On-site; INR 600,000 - 900,000; Full time

Job summary

Darwinbox Digital Solutions Pvt. Ltd. is seeking a Data Analyst to support HR Transformation and Analytics by preparing, analyzing, validating, and documenting HR data for analytics products and decision-making.

You will work with HR Analytics leaders, Business Analysts, and technical partners to translate requirements into data needs, mappings, and acceptance criteria, delivering analytical datasets and insights with strong regard for data quality and governance.

Qualifications

• Bachelor's degree or equivalent in a relevant field.
• 1+ years of experience in data analysis, BI, or analytics.
• Experience using SQL and Python to prepare, analyze, validate, and reconcile data.
• Experience with cloud-based analytics platforms or distributed databases.
• Ability translating business questions into data needs and outputs.
• Experience handling confidential information with privacy and governance in mind.

Responsibilities

• Prepare, transform, and analyze HR and workforce data from approved sources.
• Perform descriptive, diagnostic, variance, trend, segmentation analyses.
• Develop datasets, summaries, charts, and deliverables for analytics products.
• Use SQL, Python, and approved tools for repeatable data preparation and validation.
• Profile data sources for availability, quality, and suitability for use.
• Collaborate with HR analytics leaders, analysts, and technical partners.

Skills

SQL

Python

Data analysis

Data quality

Data modeling

Excel

Documentation

Education

Bachelor's degree in Business/HR/IS/CS/Data Analytics/Engineering

Tools

BigQuery

Snowflake

Databricks

Azure

AWS

Job description

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 non-technical 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.

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:

• Preferred: role-relevant certification, analytics platform training, data governance training, or Agile delivery training, as applicable.
• Preferred: Responsible AI, data privacy, data security, or HR data handling training.

Knowledge, Skills, Abilities, Behaviors:

• Ability to organize and complete assigned analytical work with appropriate guidance, review, and escalation.
• Ability to analyze data, identify trends and anomalies, and explain relevant findings and practical implications.
• Ability to validate outputs, reconcile discrepancies, document assumptions and limitations, and raise data-quality concerns.
• Ability to communicate analytical findings, work status, risks, and limitations clearly to technical and non-technical audiences.
• Ability to collaborate effectively with HR Analytics leaders, Business Analysts, subject matter experts, and technical partners.
• Strong attention to detail, organization, and ability to manage multiple assigned deliverables.
• Ability to apply established documentation, testing, version-control, data-quality, privacy, security, and governance practices.
• Ability to use approved AI tools responsibly and independently validate AI-assisted outputs before use.