About the Role
Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft. Join the People Technology Team at Adyen. At Adyen, we believe people are our greatest asset and we want to ensure we deliver the best employee experience globally. People Technology is evolving rapidly from a traditional HRIS function into a high-leverage Product & Engineering Engine.
Responsibilities
•Design, develop, and maintain high-volume ETL/ELT pipelines to extract, transform, and ingest data from Workday (via RaaS/WQL/APIs) and other People platforms into our central enterprise data warehouse.
•Create clean, scalable data models, schemas, and data marts optimized for People Analytics, executive dashboards, workforce modeling, and programmatic consumers.
•Implement automated data quality checks, testing frameworks (e.g., dbt test, Great Expectations), anomaly detection, and end-to-end lineage tracking across all People data workflows.
•Enforce strict data governance, fine-grained access controls, automated data masking, and retention rules across sensitive employee data (PII) in compliance with global privacy standards (GDPR/CCPA).
•Turn raw transactional data into reusable data products and API endpoints that feed BI dashboards, predictive workforce analytics, and generative AI features (e.g., Gemini Enterprise).
•Collaborate closely with People Analysts, Enterprise Data Engineering, Applications Engineers, and Security teams to align on data governance and eliminate manual data extracts.
Requirements
•5+ years of hands-on data engineering experience building complex ETL/ELT pipelines, data warehouses, and data models in an international environment.
•Advanced proficiency in SQL and Python for data transformation, workflow orchestration (e.g., Airflow/Dagster), transformation frameworks (e.g., dbt), and cloud data platforms (Snowflake/BigQuery).
•Experience applying software engineering best practices to data development, including version control (Git), automated testing, and CI/CD pipelines for data workflows.
•Demonstrated experience extracting and modeling HR data from Workday using RaaS reports, WQL, REST/SOAP APIs, and custom REST endpoints/webhooks.
•Hands-on experience with data quality testing frameworks (e.g., dbt test, Great Expectations) and data observability tools to guarantee data accuracy.
•Deep knowledge of employee data privacy principles (GDPR), encryption, role-based access controls, and data anonymization/masking techniques.
•Strong grasp of dimensional data modeling, data warehousing methodologies, historical snapshotting, and robust data quality frameworks.
•Excellent ability to translate complex data structures into understandable concepts for People Analytics partners and non-technical stakeholders.
•You treat data quality, privacy, and governance as baseline engineering requirements rather than afterthoughts.
•You build for scale and reuse, systematically replacing one-off manual spreadsheets with automated, self-healing data pipelines.
•You move comfortably between writing complex SQL/Python pipelines and discussing strategic workforce metrics with analytical partners.
•You are curious about modern data stack practices and explore how data foundations enable generative AI and advanced analytics.
Conditions
No conditions are stated in the posting.
