Software Engineer, School Integrations

Clever Inc.
San Francisco, CA, United States
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
4 years minimum
Compensation
$140,000.0 - $165,000.0
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Amazon S3 Batch Processing Databases Data Synchronization Cursor (Graphical User Interface Elements) Amazon DynamoDB MongoDB Node.Js Software Engineering TypeScript
+6 more
ReactJS State Machines AWS Lambda Data Pipelines Golang Microservices

Job description

The School Integrations team at Clever is responsible for the technical foundation that connects schools to our ecosystem of learning applications. As a Software Engineer on this team, you will be a key contributor to the platform that powers Clever’s products and handles the massive data volumes required to automate account creation for millions of students and teachers. You will work across the full stack-from Go microservices to React frontends-to build tools that ingest school data from various Student Information Systems (SIS). This is an incredible opportunity for an engineer to solve real-world data-scaling problems, ensuring that every student has a simple and secure connection to their learning resources on day one., * The problem you’ll solve: You will ensure that millions of users have seamless access to their learning apps by building and maintaining the high-performance data pipelines that power the Clever ecosystem.

  • Who you’ll partner with: You will collaborate closely with Product, Design, and fellow engineers to build intuitive UIs and robust backend services.
  • What success looks like: Success is measured by the delivery of robust, maintainable code and the successful execution of features that simplify complex data connections and transformations for schools.
  • Key projects you’ll lead: You will develop new features for our microservices architecture, build resilient data sync pipelines, and contribute to the internal architectures that support Clever’s massive data-processing needs.

Requirements

  • Engineering Foundation: 4+ years of professional experience as a Software Engineer with a strong foundation in software engineering principles and full-stack development.
  • Problem Solving: Proven ability to architect solutions for various product and data needs and a track record of leading small projects within a team environment.
  • Quality & Reliability: A strong commitment to writing simple, high-performing code that is easy to maintain long-term, with practical experience generating and automating tests (unit and end-to-end).
  • AI Development Tools: Familiarity with leveraging AI coding tools (such as Claude Code or Cursor) to enhance development workflows, productivity, and code quality.
  • Cultural Alignment: A commitment to understand and prioritize the Clever engineering culture (see our blog post on Defining Clever’s Engineering Culture)., * Technical Stack: Experience with our tech stack which includes Go, TypeScript, Node.js, and React, using AWS services such as lambdas and step functions, and databases/storage such as DynamoDB, S3, OpenSearch, and MongoDB.
  • Data at Scale: Knowledge of batch processing and working with data at high scale.
  • System Stability: Comfort with participating in an on-call rotation to ensure the stability of the critical data pipelines that schools rely on daily.

Benefits & conditions

  • Competitive salary
  • Flexible PTO and Paid Parental Leave
  • Comprehensive health, vision, and dental coverage
  • Mental healthcare services
  • Professional development budget
  • Annual company retreat + team events

Salary Transparency

The range of our base salary cash compensation for this role for candidates living in the United States, besides NYC and San Francisco, CA, is between $140,000-$165,000. For candidates living in NYC and San Francisco, CA, it’s between 154,000-$181,000. All final offers are determined using multiple factors, including experience and level of expertise.

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