In A Nutshell
You will 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.
Responsibilities
Partner Data Enablement (40%)
- 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.
Data Architecture and Engineering (40%)
- 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 and contribute across DataKind (20%)
- 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.
Skillset
- 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.