Why This Job is Featured on The SaaS Jobs
Modern SaaS products increasingly win on trust in their analytics layer, not just on feature breadth. This role sits in that shift, focused on data governance, lifecycle management, and observability that underpin customer-facing “Ask AI” style experiences. The remit spans ingestion planning, semantic enrichment, and the APIs and interfaces where customers define and validate their data, placing the work close to the core of how a SaaS platform proves correctness at scale.
From a SaaS career perspective, the role builds durable strengths in designing reliable systems that serve many tenants and use cases. It touches the full path from backend services and pipelines through to product surfaces, reinforcing an end-to-end delivery muscle that transfers well across analytics, platform, and infrastructure teams. The emphasis on instrumentation, incident response, and measurable improvements also aligns with how mature SaaS engineering organizations evaluate impact over time.
This position is best suited to an engineer who likes ambiguous problem spaces where definitions, schemas, and quality signals evolve, and who enjoys collaborating across product and engineering boundaries. It fits someone ready to own features independently while still benefiting from senior guidance, and who is motivated by making complex data systems understandable and dependable for customers.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About The Role & Team
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.
As a Senior Engineer, you will:
- Ship features end-to-end. Take ownership of features and improvements that measurably improve data quality, reliability, and the customer experience — from design through delivery, with support from senior teammates where needed.
- Contribute to technical discussions. Participate actively in design and code reviews, ask good questions, propose solutions, and help raise the quality bar for the work you're closest to.
- Build for reliability. Instrument the services you build, contribute to on-call rotations, respond to incidents, and leave systems better than you found them.
- Write correct, scalable code. Contribute to ingestion and governance workflows with attention to correctness, performance, and testability — applying appropriate caching, error handling, and resilience patterns.
- Help build trusted data foundations. Work on capabilities such as event/property observability (anomalies, schema drift), safe clean-up workflows, metadata ingestion, and shared data health APIs — often in collaboration with AI foundations, Pipeline/Query, and Go-to-market teams.
- Contribute to fullstack delivery. Build features spanning backend services, data pipelines, and the UI and product surfaces customers use daily.
- Partner across functions. Work with Product, Design, and peer engineering teams to understand requirements and deliver well-grounded solutions.
- Grow and help others grow. Invest in your own development, share what you learn, and contribute to a culture of feedback and continuous improvement.
You'll be a great addition to the team if you have:
- Solid fullstack engineering experience. 2–4 years delivering features that span backend services and user-facing product, including APIs and interactive UI.
- Fullstack proficiency. Working skills in JavaScript/TypeScript (Node.js and/or React) and/or Python, plus some exposure to AWS or cloud infrastructure tooling.
- A reliability mindset. Familiarity with observability basics (metrics, logs, traces) and automated testing — and a habit of thinking about what happens when things go wrong.
- Customer focus. Ability to connect your work to customer outcomes and use data to validate that what you shipped actually helped.
- Clear communication. Comfortable asking clarifying questions, flagging tradeoffs, and keeping teammates informed without being prompted.
- Nice to have. Exposure to event analytics, data governance/taxonomy, metadata systems, session replay, or AI-assisted tooling.
