Data Engineer, Part-Time

• Generate technical evidence for the assessment by working directly in the environments to extract factual information and validate interview findings with concrete data
• Inventory data sources, source systems, ingestion pipelines, and loading frequencies and windows
• Map integrations, dependencies, and single points of failure
• Identify table structures, naming conventions, and modeling patterns in use
• Document existing orchestration, version control, and observability practices
• Perform sample-based checks on critical objects for completeness, uniqueness, accuracy, and consistency across systems
• Investigate duplicate records and identifier collisions in HCP and customer master data
• Verify the existence and behavior of historical tracking versus snapshot data
• Test identifier reconciliation across different platforms
• Update the Gap & Risk Register with findings, evidence, and estimated remediation effort

• Availability for a temporary one-month assignment, working 20 hours per week
• Practical data engineering experience, including advanced SQL, Python, and ingestion and transformation pipelines
• Hands-on Databricks experience in production environments, including Spark, Delta Lake, and the ability to read third-party notebooks and jobs
• Ability to audit an environment built by another team without documentation and reconstruct its logic from the code and data
• Experience with API- and file-based integrations in batch and near-real-time scenarios
• Candidates who do not meet every requirement are still encouraged to apply

• Growth opportunities
• Values-driven culture
• International career opportunities
• Environment designed to support continuous learning
• Culture that encourages creativity, diversity, and autonomy
• Global operating model that enables integrated and seamless collaboration
• Inclusive environment with support for professional growth and development
• Accommodations during the selection process when necessary