Data Engineer

Tono, Inc.; New York (NY); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Salary 140k-190k

Equity

Medical coverage

Growth opportunities

Unlimited PTO

Job summary

Tono is building autonomous specialty medicine clinics and seeks a data engineer to own data movement across EHR, patient portals, telehealth, and clinics. You’ll shape reliable datasets used by doctors, operators, and AI features.

You’ll work with engineers, physicians, and co-founders in a fast-paced, regulated environment, ensuring data privacy, tests, and monitoring are built-in. Hybrid NY footprint preferred.

Qualifications

• 3+ years experience building and operating production data pipelines.
• Strong SQL and solid Python.
• Experience with a modern data stack: dbt, an orchestrator such as Prefect or Airflow, and a cloud warehouse such as BigQuery or Snowflake.
• Good relational database fundamentals, including schema design, migrations and query performance (Postgres).
• A habit of testing and monitoring your pipelines, and caring whether the numbers are right.
• Ability to turn a vague question from a doctor or operator into a clear dataset.
• Comfort working in a fast-moving team and picking up whatever the system needs.
• Care about privacy and data quality, and take pride in your craft.
• Nice to have: healthcare or regulated data experience, DuckDB, dlt, TypeScript, or ML/AI data support experience.

Responsibilities

• Build the data foundation for a clinic that runs on software, owning data movement from EHR, patient portal, telehealth and in-person clinic into trusted tables.
• Collaborate with engineers, physicians, care coordinators and co-founders; own pipelines from design through monitoring.
• Produce ingestion pipelines from Postgres, Stripe, Posthog into a warehouse (BigQuery, DuckDB).
• Model clean datasets in dbt covering patient funnels, clinical operations, scheduling, billing and revenue.
• Create datasets that feed AI work with labeled data, evaluation sets and clinician feedback pipelines.
• Add tests, freshness checks, lineage and alerting so the team can trust the data.

Skills

SQL

Python

data pipelines

dbt

Prefect/Airflow

BigQuery

Snowflake

PostgreSQL

testing/monitoring

data privacy

Tools

dbt

Prefect

Airflow

BigQuery

Snowflake

PostgreSQL

Job description

About Tono

Specialty care is hard to access and even harder to run. Patients often wait months, and clinics rely on fragmented tools and manual workflows.

Tono builds software and operations together to make specialty care faster to access and easier to deliver, starting with dermatology.

We’re building a fully autonomous specialty medicine clinic: continuous, real-time care for patients anywhere.

What makes us different is how we build. We run two clinics of our own, a national telehealth practice and an in‑person clinic in DUMBO, Brooklyn. Our engineers work side by side with our doctors and care coordinators to build (mostly) every piece of the software from scratch. The long‑term goal is for specialty clinics across the country to run on our autonomous EHR, freeing them from the administrative burden that slows care down.

Today we see patients in 33 states, covering more than 80% of the US population.

You will work closely with the co‑founders and a team of engineers and physicians (backgrounds include Amazon, Apple, and leading academic medical centres). We are backed by Village Global (backed by Mark Zuckerberg, Jeff Bezos, Michael Dell, chaired by Reid Hoffman), HOF Capital, and Oncology Ventures.

What You'll Do

Build the data foundation for a clinic that runs on software. You'll own how data moves from our EHR, patient portal, telehealth practice and in‑person clinic into trustworthy tables that doctors, operators and engineers rely on. The better our data, the faster we find where patients drop off, where care gets stuck, and where our AI features need to improve.

You'll work closely with engineers, physicians, care coordinators and the co‑founders, and own your pipelines from design through monitoring. On any given week you might be:

• Building and maintaining ingestion pipelines (dlt, Prefect) from our Postgres application database, Stripe, Posthog and other sources into our warehouse (BigQuery, Motherduck/DuckDB)

• Modeling clean, well‑tested datasets in dbt, covering patient funnels, clinical operations, scheduling, billing and revenue

• Building the datasets that feed our AI work, such as labelled data, evaluation sets and clinician‑feedback pipelines

• Digging into data to answer questions like where patients drop off in intake, or what slows a visit down

• Adding tests, freshness checks, lineage and alerting so the team can trust what they see

• Handling PHI carefully, with access controls, de‑identification and audit trails suited to a regulated environment

• Working with engineers on schema changes and backfills so analytics doesn't break when the product moves

We ship with discipline: small changes, clear ownership, good observability, and no data debt we can't explain.

How We Work

• We ship quickly, with discipline: small PRs, clear ownership, and tight feedback loops.

• Small team, broad scope. Engineers regularly work across frontend, backend, data, and infrastructure.

• We ship with confidence in a regulated environment: feature flags, careful reviews, and safe migrations (including backfills). We use AI all the time but we do not vibe code our way into debt. Patients and doctors deserve better.

• We invest in maintainability. Dependency upgrades, performance work, and removing dead code are part of the roadmap.

• Observability is built‑in. Logs, metrics, alerting and error monitoring are part of the job.

• We value builders. Curiosity, good taste, and a willingness to pick up whatever the system needs goes a long way.

Stack

Frontend: Next.js, TanStack Start, React

Backend: Fastify, tRPC, Prisma, PostgreSQL

Durable Workflows: Inngest

AI: Mastra, Flue, OpenAI, Anthropic, Vertex

Infra: Cloudflare, Turborepo, Doppler, AWS, GCP

Observability: Posthog

Data: Motherduck/DuckDB, dbt, dlt, Prefect, BigQuery

What We’re Looking For

• 3+ years experience building and operating production data pipelines

• Strong SQL and solid Python

• Experience with a modern data stack: dbt, an orchestrator such as Prefect or Airflow, and a cloud warehouse such as BigQuery or Snowflake

• Good relational database fundamentals, including schema design, migrations and query performance (we use Postgres)

• A habit of testing and monitoring your pipelines, and caring whether the numbers are right

• Ability to turn a vague question from a doctor or operator into a clear dataset

• Comfort working in a fast‑moving team and picking up whatever the system needs

• Care about privacy and data quality, and take pride in your craft

• Nice to have: experience with healthcare or other regulated data (HIPAA, EHR data), DuckDB, dlt, TypeScript, supporting ML or AI teams with training and evaluation data, or working at a startup

Compensation & Benefits

Salary: 140k - 190k commensurate with experience

Equity: Meaningful equity in a high‑growth, venture‑backed company

Medical: Medical, dental, and vision coverage

Growth: Direct access to founders and an agile team where your impact is highly visible. We do weekly demos with leadership

PTO: Unlimited PTO to recharge when you need it

Location

New York City preferred. We are hybrid and currently spend a few days per week in our DUMBO, Brooklyn office. We will consider fully remote for the right candidate.