Principal Software Engineer, Data Products

Accountabilities:

• • Set and demonstrate a high engineering standard through architecture, design, and code reviews, while raising expectations for testing, observability, security, reliability, and operational readiness across data-focused engineering teams.
  • Design and build the interfaces between application services, data systems, and ML systems, providing hands-on reference implementations rather than limiting contributions to architectural documentation.
  • Author and lead technical RFCs covering data, API, and application architecture, facilitating design reviews and ensuring technical decisions are effectively adopted.
  • Establish performance, security, reliability, and operational standards for application services that interact with data and machine learning systems.
  • Work directly with data scientists and data engineers to identify workflow friction and turn analytical or prototype work into secure, maintainable, scalable, and production-ready systems.
  • Ensure data products deliver meaningful customer and business outcomes while making pragmatic trade-offs between speed to market, technical quality, architecture, and long-term maintainability.
  • Advise on ML development workflows spanning experimentation, training, tracking, model registration, validation, deployment, monitoring, and retraining.
  • Explore and develop agentic AI workflows, evaluating them against real-world traffic and measurable outcomes, strengthening solutions that demonstrate value and discontinuing those that do not.
  • Identify repeated engineering patterns and turn them into reusable primitives, standards, and documentation that enable teams to solve similar problems independently.
  • Mentor and develop engineers and emerging technical leaders through pairing, coaching, knowledge sharing, and progressive ownership transitions.
  • Drive technical direction across multiple teams and deliver complex, multi-quarter initiatives without relying on direct reporting authority.
  • Promote adoption of effective standards and tooling by demonstrating their value through practical engineering outcomes and improved developer productivity.

  Requirements:

  • 10+ years of software engineering experience, with substantial ownership of large-scale production systems and a demonstrated history of establishing engineering standards.
  • Proven experience operating at a principal-level individual contributor capacity, including setting technical direction across teams, authoring RFCs, leading design reviews, and delivering multi-quarter technical roadmaps without direct authority.
  • Deep experience with distributed, data-intensive production systems, including architectural decisions driven by latency, throughput, availability, reliability, and cost.
  • Extensive Kubernetes expertise, including building and deploying containerized production services and working with autoscaling, resource constraints, resilience patterns, and deployment standards for ML workloads.
  • Strong experience collaborating with data scientists and data engineers and transforming analytical models, prototypes, or experimental work into production-grade systems.
  • Strong understanding of engineering practices for scalable, reliable, secure, and maintainable data products.
  • Demonstrated ability to build standards and tooling that engineering teams voluntarily adopt because they improve effectiveness and technical outcomes.
  • Proven mentorship and technical leadership skills, with the ability to develop engineers and cultivate the next generation of technical leaders.
  • Strong communication and collaboration skills, including the ability to influence architecture and engineering decisions across organizational boundaries.
  • Solid understanding of effective ML development workflows and the ability to advise teams building production ML capabilities.
  • Experience building agentic AI solutions for customers, including evaluation frameworks, guardrails, and observability, is a plus.
  • Hands-on experience with Databricks and its ML tooling is a plus.
  • Experience with feature-store patterns and online/offline consistency is a plus.
  • Ability to work effectively in a remote-first, globally distributed environment.

  Benefits:

  • Remote position in the United States, with location eligibility subject to applicable geographic restrictions.
  • Base salary range of $212,000–$287,000 annually, with offers generally anchored around the midpoint of the range and adjusted based on experience, skills, business needs, market value, and other relevant factors.
  • Two U.S. compensation ranges based on geographic labor-market differences, including a higher range for certain higher-cost locations such as New York City and California.
  • Equity as part of the total compensation package.
  • Medical, dental, and vision coverage, with 90% of premiums covered by the company, including dependent coverage.
  • Optional pet insurance.
  • Flexible working hours and a take-as-much-as-needed vacation policy.
  • One week-long company-wide winter slowdown.
  • Three Volunteer Days Off (VTOs).
  • Work-from-home stipend to support a home office setup.
  • Charity donation matching of up to $100.
  • Professional and career development programs, coaching, tools, resources, and an individual learning stipend.
  • Opportunities for in-person team and company gatherings through a distributed-team program, including regular off-sites and local events.
  • Inclusive hiring practices and reasonable accommodation support during the application and recruitment process.