Job Title: Data Quality & AI Readiness Product Analyst
Job Location: Hyderabad Hub
Job Type: Fulltime/Permanent
About The Job
As a Data Quality & AI Readiness Product Analyst within the MDM Jobs/Skills Taxonomy team — part of Data Governance & Master Data Management — you will sit at the intersection of data governance, Human Capital technology, and process excellence. You will be a critical enabler of Sanofi's enterprise-wide skills-based organization initiative, ensuring that the skills and jobs data powering Workday's Skills Cloud, Career Hub, and AI-driven talent matching is trusted, complete, and AI-ready.
You will drive proactive risk management, resolve global data quality issues, and ensure our Human Capital data meets Sanofi's AI-Ready Data Framework standards — making it fit to power both operational decisions and the AI-driven innovation that underpins our mission to chase the miracles of science.
Main Responsibilities
- Investigation & Diagnosis
- Assess and document downstream impact of Skills and Job Architecture data quality issues across payroll processing, management reporting, third-party integrations, and AI/machine learning model inputs
- Monitor ongoing adoption of global data standards across regions, business units, and functional teams, with particular focus on Skills and Job Architecture taxonomy data consistency in Workday — proactively detecting and flagging the re-introduction of local deviations, non-standard values, or workarounds
- Conduct structured root cause analyses to distinguish isolated errors from systemic issues requiring process or configuration-level intervention
- Use Python scripting and SQL to conduct deep-dive data profiling and root cause investigations across Workday and Snowflake data assets
- Build reusable investigation toolkits and diagnostic scripts to accelerate root cause analysis and reduce time-to-resolution across recurring issue patterns
- Support organizational cloning and data standardization initiatives through fact-based investigation, evidence gathering, and data profiling — ensuring skills data is structured and clean for AI model consumption
- Execute Data Analysis and Mapping for Workday Optimization and other relevant projects
- Data Quality Engineering & Automation
- Design and build automated Skills and Job Architecture data quality pipelines using Python to validate, profile, and monitor at scale, integrated into the Data Foundation (Snowflake)
- Contribute to the design and implementation of data observability practices — including data lineage tracking, freshness monitoring, and schema validation — across the Skills and Job Architecture data domains
- Build automated monitoring dashboards (e.g., Power BI) and alerting mechanisms to proactively surface data quality deviations before they impact downstream systems, enabling early resolution of cloning/standardization conflicts
- Data Remediation & Execution
- Develop and execute Python-based remediation scripts and automated correction workflows reducing reliance on manual EIB loads where technically feasible and accelerating remediation
- Prepare, validate, and execute data correction actions and remediation loads (EIB, manual)
- Partner closely with the Global Process Owner (GPO) and Workday Technology teams to define and implement structural fixes — whether through process redesign, system configuration changes, or governance policy updates — and deliver measurable improvement in priority data quality fields
- Governance, Risk & Stakeholder Collaboration
- Serve as a bridge between data operations and technical teams, translating business data quality requirements into actionable technical specifications aligned with MDM standards
- Identify and escalate risks to data consistency, AI readiness, and global reporting accuracy at the earliest possible stage
- Contribute to AI-Ready Data KPI scoring for the relevant data assets, including DQ rule coverage, quality scoring in Informatica CDGC, metadata cataloging, and data access classification
About You
Required Education, Experience & Skills
- Degree in Information Systems, Data Engineering, Computer Science, Data Management, or a related field
- 3–5 years of experience in data engineering, data quality, data governance, or a related analytical/technical role
- Demonstrated hands-on experience building data pipelines, validation frameworks, or automation scripts in Python
- Proven track record of conducting data investigations and delivering structured, actionable findings
- Experience working in a global, matrixed organization with cross-functional stakeholders
- Strong SQL skills for data profiling, investigation, and validation across large-scale HR datasets
- Experience with big data technologies such as Snowflake
- Experience building and maintaining ELT/ETL pipelines for data quality monitoring and remediation
- Familiarity with data remediation processes, including mass data loads and EIB (Enterprise Interface Builder) or equivalent
- Understanding of HR data domains: employee records, organizational structures, skills profiles, compensation, payroll inputs, and workforce reporting
- Experience with data quality platforms or monitoring tools (e.g., Informatica CDGC, Collibra, Ataccama, or similar)
Preferred Qualifications
- Experience in the pharmaceutical, biotech, or life sciences industry
- Experience working with Workday HCM or comparable enterprise HR platforms is a strong plus — Workday certification or formal training valued but not required as the primary technical requirement
- Familiarity with Workday Skills Cloud, Career Hub and their underlying data structures
- Exposure to MLOps or AI/ML data pipeline engineering
- Certification in data governance, data quality management, or HR analytics
- Knowledge of GDPR, data privacy regulations, and their implications for HR data management
