Senior Data Platform Engineer

At Sword, we’re building AI to heal billions and unlock humanity’s full potential. In doing so, we’re pioneering AI Care, a fundamentally new approach to healthcare built for medical reasoning, safety, and real-time treatment, not generic technology applied after the fact. As both a clinical-centric frontier AI lab and an applied AI platform, Sword is reimagining how care is delivered at scale, removing traditional barriers like appointments, waiting rooms, and stigma so more people can access the care they need—and ultimately get back to lives lived in full.

Since 2020, Sword has expanded across physical therapy, women’s health, cardiometabolic, and mental health, and is now moving beyond the session to a fully AI-native, 24/7 care program that brings physical activity, therapeutic exercise, psychotherapy, nutrition, and behavior change into one connected experience. More than 700,000 members across three continents have completed over 10 million AI sessions, helping 1,000+ enterprise clients avoid more than $1 billion in unnecessary healthcare costs. Backed by 42 clinical studies, 44+ patents, and more than $500 million raised from leading investors including Khosla Ventures, General Catalyst, and Founders Fund, Sword is defining a new standard for healthcare.

AI Proficiency at Sword

AI fluency is a core expectation at Sword. Every candidate is assessed against our three-level framework — be ready to share real examples of how AI is already part of how you work.

Explorer (Level 1) — Uses AI daily to boost personal productivity

Builder (Level 2) — Creates workflows and tools that elevate the whole team

Integrator (Level 3) — Embeds AI into products and processes at scale

Every hire must demonstrate at least Level 1. The expected level will vary depending on the seniority of the role.

Role

At Sword, data powers our mission to build a pain-free world. The Data Platform Team is building the foundation that makes data usable across clinical, operations, AI, and product teams: a modern streaming lakehouse, distributed processing for low-latency data movement, durable workflows coordinating complex operations, and an API-first surface that lets producers and consumers serve themselves - without tickets, without infra context, and increasingly through agentic interfaces. This role is for an engineer who thinks about data platforms as products. You’ve built or operated data systems at scale, you reason fluently from storage format up to query engine, and you want to shape the self-service and agentic experiences layered on top of that foundation. To get to know more about our Tech Stack, check here .

What you’ll be doing

Design and evolve Sword’s streaming lakehouse - the foundation that every data consumer in the company depends on.

Build and operate distributed streaming pipelines that move data at low latency and high reliability.

Own the durable workflows that coordinate complex data movement across systems.

Shape the platform’s API surface - the interface producers and consumers use so they never need to touch infrastructure.

Drive evaluations and integrations with vendor data platforms, sitting inside the architectural trade-offs rather than just consuming the output.

Contribute to the self-service and agentic layer: interfaces designed to be consumed by humans, systems, and AI agents alike.

Partner with data engineers and analysts on contracts, governance, and lineage.

Build and maintain AI-ready data infrastructure that powers ML and AI-driven products across Sword.

Leverage AI coding assistants and LLMs to accelerate development, automate documentation, and raise code quality.

Work in a regulated environment where audit, compliance, and governance are part of every design.

What you need to have

Proven experience designing and operating data platforms at scale - warehouse, data lake, or lakehouse architectures in production.

Hands-on experience with a modern lakehouse table format - Iceberg strongly preferred; Delta Lake or Hudi also welcome. You understand how the format works under the hood: metadata layout, snapshots, manifests, compaction, copy-on-write vs. merge-on-read.