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Sobre este puesto de Data Engineer II en Garner Health

Garner Health · Híbrido · New York City, New York

What you’ll be part of

Garner is on a mission to transform the U.S. healthcare system — and we’re the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster.

The result is a rare “win win” — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can.

About the role:

We are seeking an exceptional Data Engineer II to join our Engineering team, with a high sense of ownership who is excited about our mission, eager to learn and teach others, and can deliver across the tech stack. The ideal candidate should have experience with a modern data stack and implementations of data pipelines using Python and SQL in AWS. 

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday.

What you will do:

  • Build, optimize, and maintain data pipelines that power our business
  • Define and build out abstracted reusable data sets to be used for Business Intelligence, Marketing, and Data Science Research
  • Design, build, and evangelize a federated data validation framework to be used to monitor potential data inconsistencies
  • Protect our users’ privacy and security through best practices

The ideal candidate has:

  • 2+ years of software/data engineering experience (distributed data processing, data warehousing, data governance, Big Data, data variance, data privacy, and data quality).
  • Expertise in SQL and Python
  • Expertise in building scalable data pipelines, query optimization, data modeling, and defining reusable datasets
  • Experience working with orchestration tools (especially Airflow), databases (especially PostgreSQL), data warehouses (especially Snowflake)
  • Familiarity with SQL tuning, medallion/event-driven architectures, and telemetry.
  • Familiarity with healthcare or insurance
  • Familiarity with data security and HIPPA compliance
  • A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

What you'll get here:

You’ll work on problems that matter, at a company working to change healthcare at scale. You’ll work at the intersection of AI and systemic healthcare reform, where the problems we solve are as interesting and compelling as the mission.

At Garner, you’ll take on real, ambitious problems with real ownership and autonomy, alongside exceptional, principles-based people who genuinely want you to win. It’s demanding by design. You’ll be challenged to stretch beyond what you thought possible and receive consistent coaching to help you grow and do the best work of your career. This isn't the right fit for everyone, and that's intentional. The people here are driven by what's at stake for real people, and that's what gives our intensity its purpose. 

Technologies we use:

Postgres/SQL, Snowflake, Python, AWS, Terraform, Argo, Airflow, Spark, DuckDB, Iceberg, Airbyte, DBT, ElasticSearch (aka OpenSearch), OpenTelemetry, Looker

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Compensation Transparency:

The target salary range for this position is $138,000 - $160,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more.

Fraud and Security Notice: 

Please be aware of recent job scam attempts. Our recruiters use getgarner.com and garnerhealth.com email domains exclusively. If you have been contacted by someone claiming to be a Garner recruiter or a hiring manager from a different domain about a potential job, please report it to law enforcement here and to [email protected].

Equal Employment Opportunity:

Garner Health is proud to be an Equal Employment Opportunity employer and values diversity in the workplace. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.

Garner Health is committed to providing accommodations for qualified individuals with disabilities in our recruiting process. If you need assistance or an accommodation due to a disability, you may contact us at [email protected]. 

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Cómo se compara este salario de Data Engineer

Este puesto paga $149,000/yr — por debajo de el rango típico para los puestos de Data Engineer.

$140,337 la mediana de $215,250 $361,800

Rango típico $169,925–$271,250/yr, a partir de 73 ofertas comparables de Data Engineer en JobsRadar (salario anualizado en USD). Ver datos salariales de Data Engineer →

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