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 DataKind’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.

Requirements

Deep alignment with DataKind's mission and commitment to educational equity and data-driven social impact
Degree in Computer Science, Data Engineering, Information Systems, or related technical field (or equivalent professional experience).
At least 4 years of professional experience in data engineering, including experience leading client-facing solution architecture or infrastructure design (e.g., solutions engineering, implementation engineering).
Advanced proficiency in Python, SQL, and one or more cloud platforms (preferrably GCP).
Demonstrated experience with Databricks, or comparable data intelligence platforms.
Deep understanding of ETL/ELT pipelines, data warehousing, and data orchestration tools.
Knowledge of data governance, privacy, and security frameworks in regulated or educational environments.
Proven ability to collaborate effectively with data scientists, software engineers, data engineers, data analysts, and product managers.
Solid understanding of Software Engineering principles and the data science project life-cycle
Excellent communication skills — including the ability to translate complex technical topics for non-technical audiences and to work productively with data practitioners at partner institutions.
Comfort operating as the primary technical resource in a client setting, with an account manager present for relationship support but relying on you for all things technical.
Genuine enjoyment working with people, self-motivated, results-driven, and persistent in the face of challenges.

Benefits

Flexibility and time off
Comprehensive Wellness Support
A Culture of Growth
Meaningful Connection
Living our Values

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLETLELTdata warehousingdata orchestrationAPI developmentdata pipeline designdata integrationdata validation

Soft Skills

communicationcollaborationproblem-solvingpatiencepeople skillsself-motivatedresults-drivenadaptabilitytechnical translationclient-facing