• Designs, develops, tests, debugs and maintains software that is integrated into embedded devices and systems. • Reviews, analyzes, and translates customer requirements into the design of software products for embedded devices and systems. • Builds and writes code for embedded systems software to run on specific specialized hardware. • Integrates embedded software with hardware components. • Analyzes, monitors and improves the efficiency and stability of embedded software systems. • Tests and debugs software for embedded devices and systems.

• Write high-quality code for complex scenarios with good test coverage. • Review and translate product requirements into robust designs to ensure high-quality distributed application systems that are modular, configurable, reusable, fast, effective, user-friendly, secure, compliant, scalable, and maintainable. • Lead design and code reviews of multiple applications to ensure best practices, adherence to Mercury standards, and high quality. • Lead the sprint team to deliver new or improved features, addressing any impediments to delivering high-quality solutions in the sprint backlog. • Collaborate with other teams and stakeholders to ensure successful delivery of intended functionality. • Mentor and provide technical guidance to other engineers on the team, fostering a culture of innovation and excellence within Mercury engineering.

• Analyze, estimate, design, and implement application functionality in collaboration with engineering and product leaders • Build observable, scalable applications and services and support their operation in production • Coach and mentor teammates and partner with cross-functional colleagues • Ensure high code quality using best practices including unit and integration testing • Partner with product management to evaluate tradeoffs, propose solutions, and gather early feedback • Support modernization of legacy applications to interoperate with cloud-based, testable service boundaries • Participate in incident response and root-cause analysis to improve system reliability and prevent recurrence • Provide technical guidance and code review leadership across teams to ensure consistent engineering standards and architectural practices • Ensure systems meet security, privacy, and compliance standards appropriate for educational assessment platforms

• developing backend services using Kotlin or Java • processing large datasets using Scala and BigQuery • designing and maintaining data pipelines with Apache Airflow in the Google Cloud Composer environment • working with Google Cloud Platform to ensure scalable and efficient solutions • preparing and transforming datasets for machine learning processes • supporting AI/ML model training and experimentation • performing data analysis and presenting insights to support business decisions • collaborating with engineers, data scientists, and stakeholders throughout the project lifecycle