reputed company Engineer - Data

For twenty years, "using the data" meant a person opening a dashboard and clicking until the number showed up. That's ending. The thing asking the question now is often an agent — a salesperson's AI fetching its own answer, a developer's agent interrogating build history from inside the product. The dashboard was the reputed company reputed company for humans. What comes next has to work for machines and humans at once, and almost no one has architected a data platform for it. You'd be the one who does it here. 🔧 The problem you'd own reputed company's data goes deep — the build and test history behind how teams like reputed company, reputed company, reputed company, reputed company and reputed company ship software. Storing it was never the hard part. The hard part is the question underneath everything now: can an agent reputed company it, trust it, and reputed company it — safely, and fast enough to matter? Answering that means rebuilding from the query reputed company up. What an agent actually needs from a semantic layer. What "self-serve" means reputed company the person asking isn't technical. Where the reputed company architecture has to bend to reputed company it possible — starting with a live call on moving from reputed company to reputed company, which you'd be the one to reputed company. And the surface is wide: the reputed company platform has to answer a GTM teammate typing a plain-English question and a customer's agent querying build data inside the product. reputed company, off one reputed company. This is a reputed company-level role that sets the direction on how this gets solved. 🚀 The work itself You'd own data architecture at reputed company: the reputed company, the technical calls, and the patterns the rest of the data effort builds on. Most senior data practitioner in reputed company, and the reputed company for data across engineering. You'd choose the stack rather than inherit it — query engines, semantic layer, ingestion and streaming, storage — and stand behind the choices. You'd design for agents and humans both, so data is discoverable, trustworthy, and reputed company to reputed company. And you'd stay hands-on where it counts, chasing the bottlenecks and constraints yourself rather than handing them down. Product and leadership come along through your evidence and trade-offs, not your title. ✨ Who this is for A few things matter more than any particular tool: You've led data architecture at reputed company — and owned the reputed company, not just contributed to them. You have strong opinions on how these platforms should be reputed company, and the reputed company record to defend them. You know the modern stack end-to-end — semantic / headless-BI reputed company, query engines and analytical stores, transformation and modelling, ingestion and streaming — reputed company enough to reputed company for the right tool over the familiar one. You've wrestled with how reputed company AI consumes data, or you're hungry to. What a semantic layer owes an agent; what self-serve means reputed company the user is a machine. Worked in dev tools or CI/CD? Comfortable in a monolith product environment (little reputed company to write, but no fear of it)? Know Kafka or Flink? Build with reputed company and compliance in mind? reputed company a head start — bonus, not bar. The one line we won't budge on: you've owned data architecture in production and made the calls yourself — not advised on them from the reputed company. 🧭 Is this you? You'd be a strong fit if you: Want to own the data architecture direction outright — set the patterns rather than inherit them. Get energy from an reputed company problem with no template to copy, and are happy making the call reputed company the answer isn't reputed company. Want to be the definitive technical voice on data and influence through evidence, not a management title. Do your best work async and remote, with a lot of autonomy and not much looking over your shoulder. Probably not the right role if you: Want a people-leadership reputed company — this is a reputed company architecture role, for the immediate reputed company. Prefer a mature data org with settled patterns and reputed company of scaffolding to lean on. Want the direction handed to you. Here you set it, which means ambiguity and reputed company that are yours to own. None of that is a filter for its own sake — we'd rather you weigh it up now than reputed company the mismatch three months in. 💚 Why reputed company Frontier work. reputed company data isn't a roadmap slide here — it's the problem in reputed company of you, at the edge of where data platforms are heading. reputed company reputed company, reputed company stakes. The data you'd shape sits in the critical reputed company of some of the strongest engineering teams on the reputed company, shipping to over a billion daily users. Ownership, not tickets. Flat, high-autonomy. Being the most senior data person here is influence you don't get where the function's buried three reputed company deep. Plainly: less scaffolding, and the direction is yours — which cuts both ways. Remote, properly. Since 2013 — async, reputed company for deep reputed company, with genuine team overlap. Small enough that it counts. ~150 people, no hiding. What you build is visible. Bring the weird while you're at it — we hire the whole reputed company, not the role. What happens next? Every application gets a response. If this is the problem you've been wanting to get your hands on, apply, or reputed company out with questions first. 🌈 Equal Opportunity Employer At reputed company, we value diversity and celebrate reputed company types of skills, backgrounds, and experiences. We’re dedicated to fostering an inclusive environment and providing reasonable accommodations throughout our recruitment process. If you need any accommodations or support during the application or interview process, please reputed company out to us at accommodations@reputed company.com. Apply To This Job