Senior Software Engineer, Data Management

The Data Management team is building the Trust Engine that powers the entire product. We are moving beyond simple data entry to architecting a self-healing, intelligent data ecosystem.

As a member of the team, you will contribute to the technical vision for how customers define, govern, and trust their data at massive scale. You will help design and build systems that govern the data lifecycle, including complex ingestion planning, semantic enrichment, and data observability — spanning backend infrastructure, APIs, and the product surfaces customers interact with directly.

You will be at the forefront of transforming data governance from a manual chore into an automated, AI-driven infrastructure that powers our 'Ask AI' and Data Assistant capabilities. You are helping solve the hardest problem in analytics: proving that the data is right.

The team is fast moving and you will be expected to iterate quickly and own, drive and achieve alignment on a variety of projects ranging from new product features to best practices and scalability of our codebase & infrastructure.

• Ship meaningful product end‑to‑end. Take ownership of well-scoped features and system improvements that measurably improve data quality, reliability, and the customer experience — from design through delivery.
• Contribute to technical direction. Participate actively in design discussions, propose solutions to complex problems, and help define patterns and practices that raise the quality bar for the team.
• Uphold operational excellence. Instrument the services you build, contribute to SLO definitions, respond to incidents, and improve the reliability of systems you own.
• Build for scale and correctness. Contribute to ingestion and governance workflows with an eye toward throughput, latency, and correctness — applying appropriate caching, testing, and resilience patterns.
• Build the foundations for trusted data. Work on capabilities such as a shared data health evaluation framework and APIs, event/property observability (anomalies, schema drift), safe clean‑up workflows, and metadata ingestion — often in collaboration with AI foundations, Pipeline/Query, and Go-to-market teams.
• Expand beyond events. Help architect support for non-event data models — including entities, metrics, and dimensional data — so customers can govern their full data ecosystem within a unified taxonomy experience.
• Pioneer session replay–driven taxonomy. Contribute to using session replay signals to automatically surface untracked interactions, propose new events and properties, and close instrumentation gaps — turning passive recordings into an active, intelligent taxonomy builder.
• Own fullstack delivery. Design and ship features spanning backend services, data pipelines, and the React/TypeScript product surfaces customers use daily.
• Partner across functions. Collaborate with Product, Design, and other engineering teams (e.g., Analytics, Experiment, Ingestion/Query, Session Replay) and engage with customers and account teams to ground decisions in real needs.
• Grow and help others grow. Participate in design and code reviews, share knowledge, and invest in your own development and that of teammates.

• Strong fullstack engineering background. Experience owning and delivering features that span backend services and user-facing product, including APIs and interactive UI in distributed, microservices architectures.
• Fullstack proficiency. Solid skills in JavaScript/TypeScript (Node.js and React/frontend frameworks) and/or Python, plus experience with AWS and infrastructure tooling.
• Operational mindset. Experience with observability (metrics/logs/traces), automated testing, and CI/CD — and a habit of treating reliability as part of the job, not an afterthought.
• Customer focus. Ability to connect your work to customer outcomes and use data to validate that what you shipped actually helped.
• Engineering principles in action. Demonstrated Bias for Action, Impact > Effort prioritization, Technical Ownership, and investment in your own and your team's development.
• Clear communicator. Comfortable collaborating across stakeholders and articulating technical tradeoffs concisely.
• Nice to have. Domain experience in event analytics, data governance/taxonomy, metadata systems, session replay, non-event data models, RBAC/privacy, experimentation, or AI‑assisted automation.

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