Introduction:

University of the People (UoPeople) is the first non-profit, tuition-free, American, accredited, 100% online university. Today, UoPeople has over 170,000 students enrolled from more than 200 countries and territories, including 18,800 students who are refugees. UoPeople is accredited by the WASC Senior College and University Commission (WSCUC).

We believe that higher education is a basic human right and that it can transform not only the lives of students, but also their families’ lives, their communities, their nations, and, by extension, the world. See President Reshef’s TED Talk when he announced the founding of the University.

UoPeople is an innovative university, and we welcome team members who bring creativity and innovation to their roles. We’re a fast-paced organization with remote teams all around the globe. If you’re a self-starter who wants to succeed alongside a passionate team, we’d love to hear from you!

UoPeople is supported by the generosity of individuals and foundations, including the Gates, Hewlett, Ford Foundations, Foundation Hoffmann, and others. The University has been covered by The New York Times, BBC, NPR, Times Higher Education, U.S. News & World Report, and many other leading media outlets. President Reshef’s TED Talk and Nas Daily interview about the University have more than 30 million combined views.

Overview:

We are looking for a Data Engineer with strong hands-on experience in building and scaling data pipelines on Google Cloud, with deep expertise in BigQuery. You will design and build production-grade data pipelines, implement efficient incremental loads using BigQuery merge patterns. The role focuses on delivering reliable, cost-optimized, and well-governed data to support analytics and reporting needs.





ESSENTIAL FUNCTIONS / RESPONSIBILITIES

- Design, develop, and maintain scalable ELT/ETL data pipelines on Google Cloud Platform (GCP).
- Build and optimize BigQuery data warehouses, implementing performance and cost-optimization techniques including partitioning, clustering, materialized views, query optimization, and table optimization, while following the Medallion Architecture (Bronze/Silver/Gold layers).
- Design and implement data models for analytical workloads, following best practices for dimensional modeling, fact and dimension tables, relationships, and scalable data warehouse design.
- Develop and manage GCP Dataform workflows for SQL-based data transformation, dependency management, incremental processing, data quality validation, and automated deployment.
- Implement CI/CD practices including source control, code review, automated deployments, and managing changes across development, testing, and production environments.
- Create and maintain Google Cloud Functions and Cloud Run services to automate data ingestion, API integrations, event-driven processing, and orchestration tasks.
- Ingest, transform, and integrate data from multiple sources including Cloud Storage, REST APIs, and third-party connectors into BigQuery.
- Implement robust data quality frameworks, validation rules, automated testing, monitoring, alerting,



and observability to ensure data reliability and accuracy.
- Design and maintain secure data platforms using IAM roles, service accounts, row-level security, column-level security, data masking, and governance best practices.
- Collaborate closely with business stakeholders, analysts, and reporting teams to deliver trusted, well-documented, analytics-ready datasets and semantic data models.
- Work with Dataform, Pub/Sub, Cloud Storage, BigQuery, Cloud Functions, and Looker/Power BI to deliver end-to-end cloud data solutions.
- Design, build, and maintain cloud ML data infrastructure for managing feature tables and automating data pipelines to power seamless model training, validation and deployment.
- Stay current with emerging GCP technologies, industry best practices, and architectural patterns, proactively recommending improvements to platform design, performance, security, and cost optimization.

KEY COMPETENCIES

- 5+ years of skilled data engineering experience with strong hands-on expertise in Google BigQuery.
- Deep practical knowledge of BigQuery features including partitioning, clustering, MERGE statements for upsert/incremental loads, materialized views, scheduled queries, and cost optimization.
- Experience building end-to-end data pipelines that ingest and transform data in BigQuery.
- Strong proficiency in SQL (advanced optimization and DML) and Python (for pipeline development and automation).
- Solid understanding of data modeling, ETL/ELT patterns, and data warehousing concepts.
- Good grasp of data quality, testing, monitoring, and production reliability practices.

QUALIFICATIONS

- Bachelor’s degree in computer science, Engineering, or related field (or equivalent practical experience).

📌 Data Engineer (India)
🏢 University of the People
📍 India