HelioCampus

Data Engineer

Rol remoto de Data Engineer con fit claro de ubicación del candidato.

Publicado13 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Estados Unidos

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

Develop: Design, build, and deploy robust ETL/ELT solutions using SQL and Python.
Test & Deploy: Perform comprehensive unit testing and participate in code reviews to ensure data integrity and system reliability.
Analyze: Monitor and evaluate pipeline performance trends to proactively prevent bottlenecks and system failures.
Troubleshoot: Diagnose and resolve complex ETL issues in real-time, identifying root causes to prevent recurrence.
Collaborate: Work closely with analysts and senior engineers to expand reporting models that meet evolving university goals.

Previous experience in the Higher Education or EdTech sector.
3+ years of professional experience specifically in ETL/SQL development and data engineering.
Expertise in SQL: Strong proficiency in developing complex queries, analyzing data, and managing system integrations.
Data Architecture Knowledge: A solid understanding of data warehouse concepts, structures, and dimensional modeling.
Coding Proficiency: Experience with at least one object-oriented programming language (Python, Java, etc.).
An Analytical Mindset: The ability to think on your fly and approach technical challenges with a solution-oriented focus.
Autonomy: A drive to explore technical hurdles and find solutions with minimal supervision in a fast-paced environment.

Experience with AWS tools (Redshift, S3, Glue) or other major Cloud Service Platforms.
Exposure to NoSQL data transformation approaches.
Proficiency in translating complex business requirements into technical SQL specifications.

Higher Ed Data Warehouse Expansion: Scaling our core warehouse to accommodate new data sources and institutional partners.
KPI Modeling: Refining and expanding reporting models to track student learning outcomes and financial sustainability.
Pipeline Modernization: Optimizing existing ETL processes to leverage modern cloud-native features for faster processing.

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