Senior Software Engineer I or II - Data Platform

Senior Software Engineer I or II - Data Platform

Location: Remote, USA

How You'll Contribute to Our Mission

At Frontline Education, our mission is we transform how schools work, so every educator and student succeeds. Our vision is every school thriving. Every community stronger.

We're hiring for multiple positions at either a Senior Software Engineer I or Senior Software Engineer II level to join our Data Platform Engineering team.

The Data Platform team builds and evolves the cloud-native data capabilities that power analytics, reporting, operational insights, interoperability, and future AI-enabled experiences across Frontline's product ecosystem. Our team creates reusable platform services and trusted data foundations that allow product teams to publish, discover, govern, and consume data products at scale.

As a Senior Software Engineer, you'll help shape the next generation of Frontline's data platform. You'll contribute beyond implementation by influencing technical decisions, improving engineering practices, strengthening platform reliability, and building scalable data capabilities that support educators, administrators, and school communities. Your work will help ensure that data remains accessible, trustworthy, interoperable, and ready to support both today's business needs and tomorrow's AI-powered innovations.

How You'll Drive Success

Data Platform Engineering

• Design, build, test, deploy, and support cloud-native data platform capabilities and shared platform services.
• Develop scalable ingestion, transformation, orchestration, and data access solutions that support operational and analytical workloads.
• Build reusable and discoverable data products that enable reporting, analytics, and business decision-making across Frontline.
• Design and support distributed data workflows leveraging event-driven architectures and messaging technologies such as Kafka.
• Contribute to data modeling and persistence strategies across relational, analytical, event-oriented, and semi-structured data systems.
• Support modernization initiatives that improve scalability, interoperability, governance, and maintainability across the data ecosystem.
• Contribute to observability, resiliency, monitoring, troubleshooting, governance enablement, and operational excellence efforts.
• Partner with product engineering, reporting, and analytics teams to improve adoption experiences and reduce integration complexity.

Analytics & AI Enablement

• Help establish trusted and scalable data foundations that support reporting, analytics, operational insights, and future AI-enabled capabilities.
• Collaborate with analytics, reporting, and application teams to support self-service analytics, operational reporting, and interoperable data access patterns.
• Design solutions that improve data accessibility, discoverability, quality, governance, and operational readiness.
• Contribute to evolving AI-related platform requirements, including feature preparation, retrieval patterns, operational data access, and scalable data consumption.
• Help teams make pragmatic decisions that balance traditional analytics approaches with emerging AI opportunities.

Technical Design & Collaboration

• Participate in discovery, refinement, and design discussions to evaluate requirements, identify tradeoffs, and shape practical platform solutions.
• Collaborate closely with Product Managers, QA Engineers, Architects, Technical Leads, analytics teams, and Engineering Managers throughout the development lifecycle.
• Contribute to architectural discussions while aligning solutions to platform standards, governance expectations, and long-term engineering objectives.
• Communicate technical concepts, implementation approaches, operational considerations, and platform adoption strategies effectively to both technical and non-technical audiences.
• Build strong partnerships across geographically distributed and cross-functional teams.

Engineering Excellence

• Develop secure, scalable, maintainable, and high-performing platform solutions.
• Contribute to automated testing strategies including unit, integration, operational, and data validation testing.
• Participate in code reviews and provide thoughtful technical feedback that improves engineering quality and consistency.
• Support CI/CD automation and continuous delivery practices.
• Contribute to improvements in observability, governance, resiliency, interoperability, scalability, and developer productivity.
• Promote reusable engineering approaches, platform consistency, and sustainable development practices.
• Mentor fellow engineers and contribute to a culture of ownership, collaboration, and continuous learning.

AI-First Engineering

• Leverage modern AI-assisted development tools such as GitHub Copilot, Claude Code, OpenAI Codex, and emerging technologies to accelerate development, testing, troubleshooting, documentation, and solution exploration.
• Apply strong engineering judgment when evaluating and validating AI-generated outputs.
• Use AI to improve productivity while maintaining high standards for governance, security, maintainability, scalability, and operational integrity.
• Champion responsible and effective AI adoption across engineering workflows.

What You Bring to Help Us Grow

Placement will be determined based on experience, technical depth, scope of influence, and demonstrated impact.

Senior Software Engineer I

You will typically bring:

• Bachelor's degree in Computer Science or a related field, or equivalent professional experience.
• 5+ years of professional software engineering, platform engineering, or data platform engineering experience.
• Experience designing and building cloud-native data platform capabilities.
• Strong understanding of data ingestion, transformation, orchestration, and integration patterns.
• Experience working with event-driven architectures, distributed systems, and modern data platforms.
• Ability to independently design and deliver complex platform capabilities with high levels of quality, reliability, and maintainability.
• Experience participating in technical design discussions and evaluating implementation tradeoffs.
• Strong understanding of testing, scalability, governance, interoperability, and operational excellence.
• Experience mentoring engineers and contributing to engineering best practices.
• Experience leveraging AI-assisted development tools to improve engineering productivity while applying sound judgment and validation practices.

Senior Software Engineer II

You will typically bring:

• Bachelor's degree in Computer Science or a related field, or equivalent professional experience.
• 8+ years of professional software engineering, platform engineering, or data platform engineering experience.
• Deep expertise designing, building, and evolving large-scale cloud-native data platforms and reusable data services.
• Experience leading technical solutions that span multiple teams, data domains, or platform capabilities.
• Proven success influencing engineering standards, data architecture decisions, governance practices, and platform direction.
• Strong systems-thinking capabilities with experience balancing scalability, governance, interoperability, reliability, and customer outcomes.
• Experience driving adoption of reusable data products and self-service platform capabilities across multiple teams.
• Demonstrated success mentoring engineers and elevating engineering practices across broader organizations.
• Experience influencing technical strategy, platform modernization initiatives, analytics enablement efforts, and long-term platform evolution.
• Experience establishing effective AI-assisted engineering practices and helping teams adopt modern development workflows responsibly.
• Ability to anticipate downstream impacts and guide engineering decisions that improve long-term platform sustainability and interoperability.

Required Technical Experience for Both Levels

• Experience designing and building cloud-native data platform capabilities.
• Strong understanding of:
• Data ingestion and transformation patterns
• Event-driven architectures
• Distributed data systems
• Data interoperability and integration patterns
• Analytical and operational data workloads
• Experience with AWS cloud-native services including:
• S3
• Lambda
• EC2
• SNS/SQS
• Container-based workloads
• Data and analytics services
• Experience with:
• Kafka or equivalent messaging technologies
• Relational and analytical data systems
• Distributed data processing concepts
• Docker
• CI/CD pipelines
• Familiarity with modern data platform approaches including reusable data pro