Job Description
We are looking for a highly motivated individuals to join Tekion’s Enterprise Data Platform team. Our platform powers the organization’s data lake and serves as the foundation for a wide range of data products, analytics, reporting, AI/ML, and customer-facing experiences.
In this role, you will be responsible for building confidence in the quality, reliability, and correctness of data across the platform. You will work closely with Data Engineering, Product Management, Analytics, and AI teams to define robust validation strategies, automate testing, and ensure that data products meet the highest quality standards.
As Tekion expands its AI-powered Analytics Agent, you will also play a key role in validating user intents, defining evaluation frameworks, and ensuring the accuracy and consistency of AI-generated insights.
What You’ll Do
- Develop a deep understanding of Tekion’s enterprise data model, business domains, and data flows.
- Own end-to-end quality for data products published through the Enterprise Data Platform.
- Design and implement automated testing frameworks for data pipelines, transformations, APIs, and platform capabilities.
- Validate data correctness, completeness, consistency, freshness, and lineage across the data platform.
- Build scalable reconciliation frameworks to compare Data Lake outputs against source systems, including:
- Operational databases
- Product UIs
- Upstream services
- Business reports and dashboards
- Define data quality metrics, validation rules, and automated monitoring to detect anomalies before they impact customers.
- Partner with engineering teams to validate new platform capabilities, including ingestion, transformations, data sharing, governance, and performance.
- Design regression suites for large-scale data platform changes and migrations.
- Drive root cause analysis for production data quality issues and collaborate with engineering teams on preventive improvements.
- Define and execute test strategies for AI-powered Analytics Agent capabilities by:
- Validating user intents
- Creating evaluation datasets (evals)
- Measuring response accuracy and consistency
- Identifying hallucinations and edge cases
- Continuously improving evaluation coverage
- Champion quality engineering best practices across the Data Platform organization.
