Senior Software Engineer

Frontline Education

Location: USA
Workplace: Remote
Employment: Full Time
Salary: USD 95,000–150,000/yr

Posted 2mo ago

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Responsibilities

Design, build, test, deploy, and support cloud-native data platform capabilities and shared platform services.
Develop scalable ingestion, transformation, orchestration, and data access solutions.
Build reusable and discoverable data products that enable reporting, analytics, and business decision-making.
Design and support distributed data workflows leveraging event-driven architectures and messaging technologies.
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.
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.
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.
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.
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.
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.

Requirements

5+ years of professional software engineering, platform engineering, or data platform engineering experience (Level I)
8+ years of professional software engineering, platform engineering, or data platform engineering experience (Level II)
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.
Deep expertise designing, building, and evolving large-scale cloud-native data platforms and reusable data services (Level II)
Experience leading technical solutions that span multiple teams, data domains, or platform capabilities (Level II)
Proven success influencing engineering standards, data architecture decisions, governance practices, and platform direction (Level II)
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 (Level II)
Experience influencing technical strategy, platform modernization initiatives, analytics enablement efforts, and long-term platform evolution (Level II)
Experience establishing effective AI-assisted engineering practices and helping teams adopt modern development workflows responsibly (Level II)
Ability to anticipate downstream impacts and guide engineering decisions that improve long-term platform sustainability and interoperability.
Strong understanding of: Data ingestion and transformation patterns
Strong understanding of: Event-driven architectures
Strong understanding of: Distributed data systems
Strong understanding of: Data interoperability and integration patterns
Strong understanding of: Analytical and operational data workloads
Experience with AWS cloud-native services including: S3
Experience with AWS cloud-native services including: Lambda
Experience with AWS cloud-native services including: EC2
Experience with AWS cloud-native services including: SNS/SQS
Experience with AWS cloud-native services including: Container-based workloads
Experience with AWS cloud-native services including: Data and analytics services
Experience with: Kafka or equivalent messaging technologies
Experience with: Relational and analytical data systems
Experience with: Distributed data processing concepts
Experience with: Docker
Experience with: CI/CD pipelines
Familiarity with modern data platform approaches including reusable data products, self-service platform capabilities, and data mesh concepts.
Experience working within Agile software development environments.
Strong communication, collaboration, and problem-solving skills.

Preferred

Experience with Snowflake, Databricks, Redshift, or similar analytical platform technologies.
Experience with analytics enablement platforms and reporting ecosystems.
Experience building shared platform capabilities consumed across multiple product teams.
Experience supporting AI or machine learning enablement through scalable data platform design.
Familiarity with governance concepts including lineage, discoverability, access control, metadata management, and data quality.
Experience with distributed streaming or CDC-based architectures.
Familiarity with Kubernetes or container orchestration platforms.
Experience working within multi-tenant SaaS environments.
Experience collaborating with geographically distributed engineering teams.
Experience leveraging AI-assisted or agentic development workflows in professional software engineering environments.

Skills

AWS
S3
Lambda
EC2
SNS
SQS
Kafka
Docker
CI/CD
Snowflake
Databricks
Redshift
Kubernetes

Responsibilities

Requirements

Preferred

Skills

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