Top Healthtech Data Engineering Jobs in New York City, NY

Design and implement SQL transformations and datasets, manage data models in Databricks, optimize performance, and ensure compliance with data governance standards.

The Data Engineer will design and maintain data pipelines on Azure, manage ClickHouse for analytics, and ensure healthcare compliance while supporting ML integration.

The Data Integration Engineer will manage data integrations and ETL processes for healthcare data, ensuring accuracy and accessibility for business users.

The Associate Director of Audience Ingestion leads a team to manage audience data onboarding and ingestion workflows, ensuring data quality and optimized processes while collaborating with cross-functional teams.

The Analytics Engineer will build data models and pipelines for reporting and analytics, improving data infrastructure for ML and AI products, and setting processes for data collection and analysis.

The role involves building and owning data pipelines, maintaining data quality, and creating reporting systems that provide insights to various teams. The candidate will also design AI-ready data infrastructure.

The Data Engineer will design and maintain data pipelines, collaborate with teams on data needs, and ensure data integrity and quality while leveraging AWS services.

The Senior Data Engineer will design and develop scalable data architectures, manage ETL processes, optimize databases, and implement MLOps pipelines, promoting data-driven decision-making across the organization.

The Lead Analytics Engineer will define and expand Oscar's unified analytics data foundation, focused on dimensional models and semantic layers, while leading cross-functional projects and mentoring junior team members.

Design, build, and maintain dbt-based analytics-ready data models and curated data marts powering a PBM financial risk engine. Onboard and optimize claims, pricing, contract, and utilization data, implement data quality controls, support forecasting and risk metrics, and enable advanced analytics and ML operationalization for finance and underwriting stakeholders.