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Adyen is seeking a People Technology Data Engineer to lead the data engineering and analytics infrastructure for the People organization. You will design, build, and maintain high-performance data pipelines and scalable data models that ingest employee data from Workday and satellite systems into the central enterprise data platform.
Key responsibilities include:
• Build and scale People data pipelines: Design and develop high-volume ETL/ELT pipelines to extract, transform, and ingest data from Workday (via RaaS/WQL/APIs) and other People platforms into the central data warehouse.
• Architect People data models and data marts: Create clean, scalable data models, schemas, and data marts optimized for People Analytics, executive dashboards, workforce modeling, and programmatic consumers.
• Ensure data quality, lineage, and performance: Implement automated data quality checks, testing frameworks (dbt test, Great Expectations), anomaly detection, and end-to-end lineage tracking across all People data workflows.
• Embed security, privacy, and data governance: 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).
• Power analytics and AI use cases: Turn raw transactional data into reusable data products and API endpoints that feed BI dashboards, predictive workforce analytics, and generative AI features.
• Partner across Data & Analytics: Collaborate closely with People Analysts, Enterprise Data Engineering, Applications Engineers, and Security teams to align on data governance and eliminate manual data extracts.
You will treat data quality, privacy, and governance as baseline engineering requirements, build for scale and reuse, and move comfortably between writing complex SQL/Python pipelines and discussing strategic workforce metrics with analytical partners.
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
• Experience with workflow orchestration (Airflow/Dagster) and transformation frameworks (dbt)
• Hands-on experience with 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 (dbt test, Great Expectations) and data observability tools
• 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
