Data Engineer; Remote

Data Kind is looking for a Data Engineer!

Data Kind is looking for a values-driven, client-facing data engineer who is ready to make a major impact on student graduation rates by building and maintaining data pipelines in close partnership with higher education institutions. If you're a technically strong problem-solver who genuinely enjoys working with people, can hold down the technical side of a client engagement independently, and are committed to advancing equity through data — we want to bring you on board!

Location

This is a remote position that can be based anywhere in the U.S. and requires the majority of working hours each day to fall between 8am and 6pm Eastern Standard Time.

This position requires travel across the United States, expected at about 20% of working days, mostly on-site at higher education institutions to provide direct technical support.

This role reports to the Data Science Manager and sits within the Data Science team.

Salary Range

The salary range is $106,000 - $120,000.

Actual salary within these ranges will be based on the candidate’s experience and internal salary equity scan of active employee(s) with similar role and experience.

About the Opportunity

Data Kind is seeking a Data Engineer to lead the technical work that makes our UDTS platform — an end-to-end data platform for higher education — genuinely useful to the institutions we serve. This role sits at the intersection of data architecture, engineering, and client partnership, enabling educational institutions to use AI and advanced analytics to improve student outcomes equitably.

Reporting to the Senior Data Science Manager, you'll work across two complementary tracks: building and maintaining the infrastructure that powers UDTS, and going on-site with partner institutions to get their systems connected, configured, and delivering value to their teams. You'll collaborate closely with Engineering, Data Science, Customer Success, Research, and Product — and you'll often be the sole technical voice in the room with a client.

What You’ll Do

• Lead the technical onboarding of partner institutions onto UDTS, designing and building the integrations (APIs, pipelines, etc.) needed to connect their existing data systems for seamless ingestion.
• Collaborate on-site with institutional data teams to configure and customize UDTS front-end dashboards — working through requirements like which fields to surface, how data should aggregate, and how to make the platform most relevant to each institution's specific workflows and users.
• Support institutions in securely sharing data and implementing governance protocols.
• Communicate technical requirements, timelines, and processes with external partners in accessible, solutions-oriented ways — and bring the patience and people skills to make those conversations productive.

• Design, build, and maintain scalable data pipelines and architectures to support education-focused data products.
• Develop robust APIs and integrations to facilitate seamless data exchange across multiple institutional systems and data providers.
• Optimize data ingestion, transformation, and validation processes for reliability, transparency, and performance.
• Ensure all data systems comply with Data Kind’s governance, privacy, and security standards.
• Produce clear, reusable code and comprehensive documentation for both technical and non-technical audiences.

• Collaborate with team members within the technology team to set engineering standards and guide data infrastructure strategy.
• Partner with data engineers and scientists to operationalize models and analytics pipelines into production-ready systems.
• Collaborate with Customer Success, Research, Product, and Implementation teams to ensure technical design aligns with user and partner needs.
• Contribute to the continuous improvement of internal tooling, workflow automation, and best practices.

Qualifications

• Deep alignment with Data Kind's mission and commitment to educational equity and data-driven social impact
• Degree in Computer Science, Data Engineering,…