Stand Together - Senior Data Platform Engineer

• Design, implement, and maintain an automated and extensible testing framework for the data platform, including but not limited to: • Unit testing for data transformations • Integration testing across datasets, models, and platform components • Regression testing for output consistency • Schema and data contract validation • Invariant testing for core business rules • End-to-end pipeline testing • Performance and scalability testing • AI prompt regression testing • AI evaluation testing • Telemetry validation for AI and data systems • Establish development-time safeguards to validate business logic and expected behavior prior to release. • Contribute to quality standards, release criteria, and reliability practices for core data products and public-facing integrations. • Partner with data engineering, analytics engineering, product, and AI/application teams to embed testing into development workflows. • Identify structural quality gaps and recommend scalable solutions. • Contribute to platform resilience planning, including disaster recovery and failover readiness. • Document testing standards, practices, and reusable frameworks for cross-team adoption. • 5+ years of experience in Data Engineering, DevOps, ML Ops, ML Engineering, Analytics Engineering, and/or Site Reliability Engineering. • Experience designing or implementing automated testing frameworks for data pipelines, ETL/ELT systems, analytics platforms, or similar environments. • Strong SQL skills and experience validating complex transformation logic. • Experience with modern data platforms, orchestration tools, and data quality practices. • Demonstrated ability to translate business rules into automated validations and testing coverage. • Experience partnering with cross-functional stakeholders to improve engineering quality and release confidence. • Experience with semantic-layer architectures and downstream BI or application consumption. • Experience supporting public-facing or customer-facing data products. • Familiarity with AI/LLM-enabled systems and related quality practices, including prompt regression testing or evaluation frameworks. • Experience with observability or monitoring tools for data systems. • Experience defining platform standards or leading quality initiatives across teams. • Familiarity with disaster recovery, resilience, and failover planning for data platforms. • Enthusiasm to contribute to Stand Together's vision and principled approach to solving problems, and a commitment to stewarding our culture, which champions values including transformation and innovation, entrepreneurialism, humility, and respect.