What is the salary for this Senior Data Engineer role?

Salary information is not publicly listed for this position. Apply directly to discuss compensation with Regard.

Where is this Senior Data Engineer position located?

This is an on-site position at Regard located in New York, NY.

How do I apply for this Senior Data Engineer job at Regard?

Click the 'Apply' button on this page to be redirected to Regard's application portal. Make sure to have your resume ready and tailor your application to highlight relevant experience.

Regard is actively hiring for Data roles. Visit the company page to see all open positions and learn more about working at Regard.

Senior Data Engineer

Regard New York, NY Today

data

As a Senior Data Engineer at Regard, you will own the data pipelines and infrastructure that turn raw data into the metrics and insights that drive our product decisions and research. We run an engineering-first stack that prioritizes transparent, code-driven systems over black-box services, and you will be responsible for architecting and evolving these systems.

You will set the technical standards and take full ownership of our data platform's reliability, performance, and data quality, going beyond basic monitoring to tackle real operational challenges. This includes analyzing and tuning Spark workloads and partitioning strategies to control costs, handling breaking upstream changes, and enforcing rigorous data quality standards so our analytics are as dependable as our application code.

About Regard

Our mission is to bring world-class healthcare to everyone. Regard is an AI-powered Proactive Documentation platform that advances how care is delivered by reviewing all patient data in the EHR to recommend diagnoses and surface clinical evidence. Regard drafts a note even before the physician sees the patient, enabling an approach that gets documentation right at the point of care - we call it Proactive Documentation. This improves quality of care, reduces physician burden, and improves hospital finances. We are excited by challenges, mission-oriented work, and meaningful relationships. We work closely with some of the top health systems in the country and are leading the change that healthcare - one of the largest and most inefficient industries in the world - needs. We want you to join us.

Our Tech Stack:

Data: Apache Iceberg, EMR, PySpark, Dagster, Clickhouse, PostgreSQL, Metabase

Backend: Python (FastAPI, Flask), Redis, Celery

Infrastructure & DevOps: Docker, Kubernetes, AWS (SQS, SNS), GitLab CI/CD

APIs & AI: OpenAPI, OpenAI (GPT models)

Responsibilities:

Collect, model, and consolidate data into the data platform to support analytics, ML development, and research initiatives

Design, build, and evolve data models and pipelines that reliably transform and deliver data to downstream consumers

Own data quality in collaboration with engineering teams, ensuring datasets are trustworthy and production-ready

Partner closely with product to deliver analytics and actionable insights to internal and external stakeholders

Own the reliability and day-to-day operation of the data platform and its pipelines through proactive monitoring, alerting, and operational management

Minimum Qualifications:

Bachelors degree in Computer Science, Mathematics, Statistics, or a related field, or equivalent practical experience

5+ years of experience in data engineering roles building and operating production data pipelines

3+ years of experience working with public cloud platforms (e.g., AWS services such as Glue, Kinesis, or EMR)

Strong proficiency in Python and SQL

Hands-on experience across the full data stack, with particular depth in data modeling and pipeline design

Willingness to participate in on-call operational support for owned systems

Ability to travel up to four weeks per year for company co-working and/or retreat weeks

Strong verbal and written communication skills

Preferred Qualifications:

Experience working with healthcare data, including HIPAA compliance, data de-identification, and familiarity with open data standards such as OMOP CDM

Experience building and supporting data pipelines for ML workflows, including model training, validation, deployment, and ongoing performance evaluation

Hybrid Work | Location | Work Authorization

For this role, Regard is currently only considering candidates who are authorized to work in the US without visa sponsorship, and are within the New York City, Los Angeles, or San Francisco metro areas

We expect our Engineers to be in the office on Tuesdays and Thursdays. We may request more frequent in-office work during the onboarding period

We will provide relocation assistance to anyone who does not already reside in the NYC metro area

We prefer hiring people within commuting distance of our offices because we value getting together in person regularly

For those who enjoy working from our LA or Manhattan offices on a more regular basis, we offer catered lunches and other fun perks

Additionally, hybrid employees have the flexibility to work from locations outside of their home office from up to 6 weeks per year

Comp | Perks | Benefits

Eligible for equity

99% employer paid health benefits (Medical, Dental, and Vision) + One Medical subscription

18 PTO days/yr + 1 week holiday break

Monthly health & wellness budget

Company-sponsored team retreat + social events

A sabbatical program

Our goal at Regard is to provide and maintain a work environment that fosters mutual respect, professionalism and cooperation. Regard is proud to be an equal opportunity employer that does not discriminate on the basis of actual or perceived race, creed, color, religion, national origin, ancestry, alienage or citizenship status, age, disability or handicap, sex, gender identity, marital status, familial status, veteran status, sexual orientation or any other characteristic protected by applicable federal, state or local laws. We celebrate diversity and are proud of our supportive, inclusive workplace.

All candidates must successfully complete a background check as part of the hiring process.