Back
Strong employer brand, mid-level generalist data role, metro location, and common skillset increase applicant competition.
Data engineering skills transfer across industries, but HR/Workday specialization increases domain sensitivity.
Explicit 3–5 years plus mandatory Python/SQL/Snowflake and data governance skills raises filtering strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead investigation, diagnosis, and resolution of global data quality issues in Skills and Job Architecture taxonomy data within Workday and connected systems.
Design, build, and automate data quality pipelines and monitoring dashboards using Python, SQL, and Snowflake to ensure AI-readiness for Human Capital data.
Collaborate with Global Process Owner and technology teams to implement structural fixes and governance updates for measurable improvements in priority data fields.
Degree in Information Systems, Data Engineering, Computer Science, Data Management, or related field.
3–5 years of experience in data engineering, data quality, data governance, or related analytical/technical role.
Proficiency in Python and SQL for building data pipelines, validation frameworks, and conducting data investigations.
Experience with big data platforms like Snowflake and data quality tools (e.g., Informatica CDGC, Collibra). Work Experience Required: 3–5 years.
Experienced in working within global, matrixed organizations involving cross-functional stakeholder collaboration.
Background in HR data domains such as employee records, compensation, payroll, and skills profiles, preferably with exposure to enterprise HR platforms like Workday.
Proven ability to deliver technical automation and structured root cause analysis, enabling data governance and AI readiness of complex data assets.