Senior / Staff Software Engineer - Integration Team

New

United StatesFull-TimeStaff

SalaryThe salary for this role will be between USD $160,000 to $245,000 per year.

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Job Details

Experience: 5+ years of backend software development experience, including integration, data parsing, or log processing work, with 1+ years as a technical lead or proven experience in taking full ownership of a project.
Required Skills: GraphQLPythonJavaC++GoRESTful APIs

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
5+ years of backend software development experience, including integration, data parsing, or log processing work.
1+ years as a technical lead or proven experience taking full ownership of a project.
Strong proficiency in at least one general-purpose programming language, such as Python, Go, Java, or C++, with a track record of production-quality code.
Experience with API design and integration, including REST, GraphQL, or streaming APIs.
Experience with secure API patterns such as OAuth, API keys, and rate limiting.
Understanding of log and data formats including JSON, XML, CSV, syslog, key-value, and unstructured text.
Experience with regular expressions, data normalization, and schema design.
Demonstrated regular production use of AI tools such as Copilot, Cursor, Claude, or ChatGPT, with judgment to evaluate their output.
Experience building and operating microservices in production is preferred.
Experience with large-scale data processing, real-time pipelines, event-driven architectures, distributed systems, message queues, or orchestration frameworks is preferred.
Familiarity with cloud platforms, logging services, and containerized deployments is preferred.

Own one or more integration domains end-to-end, including roadmap, architecture decisions, trade-offs, and delivery.
Evolve shared integration frameworks, including parser frameworks, by designing reusable primitives and improving performance, reliability, and testability.
Design schema mappings and normalization approaches for ambiguous or malformed data.
Partner with detection and data teams to ensure ingested data supports downstream use cases.
Mentor junior engineers through design discussions and code reviews.
Use LLM-based coding assistants, AI-driven testing, and automated code review to accelerate design and development.
Use LLMs to analyze log samples and propose parsing rules, applying human judgment to validate the output.
Automate engineering tasks such as builds, deployments, monitoring, incident triage, parser regression testing, and schema diffing.
Promote AI tool adoption across the team and build internal tooling where useful.

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