Data Quality & AI Readiness Product Analyst

Hely: Budapest
Cég: Sanofi
Kategória: IT programozás, Fejlesztés

1. Investigation & DiagnosisAssess 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 inputsMonitor 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 workaroundsConduct structured root cause analyses to distinguish isolated errors from systemic issues requiring process or configuration-level interventionUse Python scripting and SQL to conduct deep-dive data profiling and root cause investigations across Workday and Snowflake data assetsBuild reusable investigation toolkits and diagnostic scripts to accelerate root cause analysis and reduce time-to-resolution across recurring issue patternsSupport 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 consumptionExecute Data Analysis and Mapping for Workday Optimization and other relevant projects2. Data Quality Engineering & AutomationDesign 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 domainsBuild 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 conflicts3. Data Remediation & ExecutionDevelop and execute Python-based remediation scripts and automated correction workflows reducing reliance on manual EIB loads where technically feasible and accelerating remediationPrepare, 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 fields4. Governance, Risk & Stakeholder CollaborationServe as a bridge between data operations and technical teams, translating business data quality requirements into actionable technical specifications aligned with MDM standardsIdentify and escalate risks to data consistency, AI readiness, and global reporting accuracy at the earliest possible stageContribute 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 Degree in Information Systems, Data Engineering, Computer Science, Data Management, or a related field3–5 years of experience in data engineering, data quality, data governance, or a related analytical/technical roleDemonstrated hands-on experience building data pipelines, validation frameworks, or automation scripts in PythonProven track record of conducting data investigations and delivering structured, actionable findingsExperience working in a global, matrixed organization with cross-functional stakeholdersStrong SQL skills for data profiling, investigation, and validation across large-scale HR datasetsExperience with big data technologies such as SnowflakeExperience building and maintaining ELT/ETL pipelines for data quality monitoring and remediationFamiliarity with data remediation processes, including mass data loads and EIB (Enterprise Interface Builder) or equivalentUnderstanding of HR data domains: employee records, organizational structures, skills profiles, compensation, payroll inputs, and workforce reportingExperience with data quality platforms or monitoring tools (e.g., Informatica CDGC, Collibra, Ataccama, or similar) Experience in the pharmaceutical, biotech, or life sciences industryExperience 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 requirementFamiliarity with Workday Skills Cloud, Career Hub and their underlying data structuresExposure to MLOps or AI/ML data pipeline engineeringCertification in data governance, data quality management, or HR analyticsKnowledge of GDPR, data privacy regulations, and their implications for HR data management An international work environment, in which you can develop your talent and realize ideas and innovations within a competent teamBring the miracles of science to life alongside a supportive, future-focused teamAn environment based on last technologies and frequent training to reinforce your profileBe part of a simpler, digital- and AI-powered business that’s rethinking how we work and engage with the world.

Címkék: Employee status, College

profession.hu - kb. 7 órája

További információ és jelentkezés