Senior Data Engineer (NYC Hybrid)

Empassion.Com; New York (NY); On-site; USD 100,000 - 180,000; Full time

Job summary

Empassion.Com is seeking a data engineer to build scalable data pipelines and analytics workflows that power data-driven care insights. You will develop Airflow DAGs, dbt models, and Python components across cloud data warehouses.

Collaborate with product and analytics teams in a fast-paced startup environment, champion data quality, and translate business needs into robust data solutions. Strong SQL, cloud experience, and a proactive, collaborative mindset are essential.

Qualifications

• 2+ years in data engineering or analytics engineering.
• Strong SQL and Python experience.
• Experience with Airflow and dbt in cloud environments.
• Familiarity with cloud data warehouses (GCP preferred).

Responsibilities

• Partner with teams to understand data needs and deliver reliable pipelines and models.
• Build and maintain scalable ingestion and egress pipelines in Airflow and dbt Cloud.
• Implement unit tests and monitoring to guarantee data integrity.
• Model, transform, and structure healthcare datasets into usable formats for data science models and reporting marts.
• Enhance and scale data models with SQL and dbt for new partnerships.
• Write Python code for Apache Airflow DAGs and utilities to orchestrate data workflows.

Skills

Airflow

dbt

Python

SQL

BigQuery

Cloud Storage

Git

Tools

GitHub

Job description

Job Overview

Empassion is a Management Services Organization (MSO) focused on improving the quality of care and costs for the advanced illness/end‑of‑life patient population, representing 4 percent of the Medicare population but 25 percent of its costs. The impact is driven deeper by families who are left with minimal options and decreased time with their loved ones. Empassion enables increased access to tech‑enabled proactive care while delivering superior outcomes for patients, their communities, the healthcare system, families, and society.

Responsibilities & Qualifications

• Partner with teams across the business and external partners to understand data needs and deliver reliable pipelines and models that solve real problems.
• Build and maintain scalable ingestion and egress pipelines in Airflow and dbt Cloud, ensuring high quality, automated data flows across cloud environments.
• Implement unit tests and monitoring to guarantee data integrity and reproducibility.
• Model, transform, and structure healthcare datasets into usable formats that power data science models and other reporting marts.
• Enhance and scale data models with SQL and dbt, ensuring precision and adaptability for new partnerships.
• Write Python code for Apache Airflow DAGs, components and utilities that orchestrate and monitor data workflows, building complex pipelines that enable flexible scheduling, conditional logic, and smooth integration across multiple data sources.
• 2+ years in data engineering or analytics engineering with proven ability to build pipelines and scalable workflows.
• Strong SQL skills for querying large, complex datasets.
• Proficiency in Python for data engineering tasks (transformations, APIs, automation).
• Experience with cloud data warehouses and storage (GCP preferred: BigQuery, Cloud Storage, Composer; AWS/Azure equivalents acceptable).
• Hands‑on experience with dbt or similar data‑modeling tools.
• Comfort working in collaborative dev/staging/prod environments, partnering with Product and Tech to safely test, launch, and anticipate the impact of new changes.
• Curiosity about operational workflows and a drive to partner with non‑technical teams, ensuring data and reporting align with how the business actually runs; you’re not just a spec‑taker, you’re part of the solution.
• A proactive, problem‑solving mindset and ability to thrive in fast‑paced, iterative environments.
• Strong communication skills to collaborate with analysts, engineers, and business stakeholders.

Nice to Have

• Knowledge of healthcare data (claims, ADT feeds, eligibility files).
• Familiarity with Git/GitHub for version control.
• Early‑stage startup experience (seed/Series A), especially mission‑driven ones.
• Experience building semantic layers and data models in Looker (LookML).