About the job
Data Engineer
Location: Boston, MA (Hybrid — 2 days per week in office)
About Us
Clasp is a dynamic, venture-backed startup dedicated to reshaping access to education and career opportunities. We are on a mission to transform how employers attract and retain vital talent while addressing the student debt crisis head-on. Our innovative platform connects employers, educational institutions, and diverse talent, utilizing accessible education financing as a core connection point. More than just a fintech company, we aim to be a catalyst for economic mobility.
As a proud member of the Forbes Fintech 50 and a portfolio company of the Society of Human Resource Management (SHRM), Clasp has been recognized as the 'Startup of the Year' by StartUp Boston. Our commitment to social impact and innovation drives us to reshape the workforce's future, one opportunity at a time. Join us in empowering learners and unlocking fulfilling careers that foster positive change within their communities and beyond.
The Role – Data Engineer
We are looking for a skilled Data Engineer to develop and manage robust data pipelines that will ingest and provide access to critical data concerning hundreds of millions of dollars in student loans, education enrollment statuses, and employment metrics. The ideal candidate will possess strong programming skills in Python and a fervor for high-performance data pipelines and the analytics they enable.
At Clasp, we cultivate a DevOps culture where engineers have complete ownership of their code and the infrastructure it operates on. We seek candidates who are eager to make significant contributions to the architecture that supports our product roadmap and the Stride business, while also pursuing substantial personal growth alongside us!
Our modern Data Technology Stack includes Airflow, dbt, PostgreSQL, Superset, BigQuery, Python, and SQL.
Key Responsibilities
Data Pipeline Development & Reliability
Design and maintain scalable data pipelines for ingesting and transforming financial, educational, and employment data.
Ensure data reliability, timeliness, and accessibility for internal teams and external partners.
Proactively identify and resolve data quality issues, including upstream dependencies.

