Senior Data Platform Engineer

Stand Together•Arlington, VA

1d•Remote

About The Position

Stand Together is a philanthropic community that helps Americaâ€™s boldest changemakers tackle the root causes of our countryâ€™s biggest problems, from education to the economy, broken communities, and toxic division, among dozens of other pressing issues. We provide our partners with access to resources including funding, thought leadership, a network of peers, and a playbook for applying proven principles to transform lives and society. We are seeking a Senior Data Platform Engineer to join our Data Platform and Engineering team. The Senior Data Platform Engineer will establish and own a layered automated testing and validation framework across the data platform. As data consumption expands across business intelligence tools, custom applications, semantic-layer access patterns, and AI-enabled interfaces, this role will ensure platform reliability, correctness, and scalability through stronger development-time quality controls. This position is responsible for creating the testing and validation foundation needed to reduce manual QA burden, improve release confidence, and mitigate business risk associated with public-facing and AI-enabled data consumption. The role will focus initially on establishing a reliability baseline across core data products and public-facing integrations, then provide ongoing shared ownership of the data platform and its continuous improvement. This position is open to both HQ-based and remote candidates.

Requirements

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.

Responsibilities

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.