Data Engineer
Skills
About the role
This is a remote-first senior data engineering role in the United States for building a healthcare technology organization’s analytics and AI data platform. It suits a hands-on data engineer with warehouse, pipeline, modeling, reliability, and cross-functional platform experience. You’ll create the data systems and agent-assisted workflows that support analytics, product insights, and AI use cases.
What you’ll do
- Build and operate warehouse, transformation, orchestration, and data-serving layers.
- Develop ingestion and ELT or ETL pipelines for product, CRM, marketing, finance, and support data.
- Create tested dimensional and semantic models for consistent metrics and entities.
- Implement observability, anomaly detection, lineage, SLAs, and incident-response practices.
- Work with engineering teams on event taxonomies, tracking plans, and data contracts.
- Improve data-platform agents that monitor failures, identify regressions, generate initial SQL or dbt work, and recommend remediation changes.
What they’re looking for
- At least five years building and operating production data pipelines and warehouse models.
- Strong SQL and experience with a cloud warehouse such as BigQuery, Snowflake, or Redshift.
- Experience with dbt or equivalent transformation tools, orchestration tools, and Python automation.
- A reliability-focused approach to testing, observability, incident response, SLAs, and cost control.
- Hands-on ability to design and evaluate agent workflows grounded in data lineage and documentation.
- Ability to influence Product, Engineering, Analytics, and go-to-market partners.
What’s on offer
- Remote-first work with a USD 50 monthly work-from-home reimbursement.
- A new Mac laptop and hardware when joining.
- Medical, dental, vision, disability, life, and accident coverage.
- A USD 40 monthly wellness stipend, HSA/FSA options, EAP, and adoption assistance.
- 401(k) matching after three months, discretionary PTO, 13 paid holidays, and paid parental leave.
Questions about this role
Is this Data Engineer role remote?
Yes. The posting describes a remote-first team.
How many years of experience are required?
The role requires at least five years building and operating production data pipelines and warehouse models.
What tools does the Data Engineer need experience with?
The posting calls for SQL, a cloud warehouse, dbt or equivalent tooling, an orchestration platform, and Python.
Does the role include AI work?
Yes. The engineer will develop and improve data-platform agents and support data foundations for AI.
Related roles
Junior Product Manager
Cipherhealth is hiring a remote Junior Product Manager to own a B2B SaaS product area from discovery through delivery. The role suits a product professional with 3–7 or more years of experience who can combine customer evidence, cross-functional delivery, and AI-assisted workflows.
Lead Data Engineer – Databricks & AI | Remote Canada
Lead data engineer role for a financial services client, requiring 10+ years of Databricks expertise and experience collaborating with AI/ML teams to build scalable data infrastructure.
Data Analyst - Data Quality & Databricks
A data analyst role centered on data quality, profiling and reconciliation using Databricks, SQL and PySpark, open across Bangalore, Chennai and Pune for candidates with 7-11 years of experience.
Data Engineer, Data Platform & ML (Remote)
A Poland-remote Data Engineer role building a cloud-based corporate data warehouse for business analytics and machine learning. It suits a data engineer with at least three years of experience in Python, SQL, warehouse design, and modern data tooling.
Data Engineer, Data Platform & Machine Learning (Remote)
A Serbia-remote data engineering role building Tabby's corporate data warehouse, data integrations, and pipelines. It is intended for a data engineer with warehouse design, Python, SQL, and cloud data-stack experience.
Senior Data Engineer, Data Platform & ML (Remote)
A remote senior data engineering role in Yerevan focused on feature-store development, data services, streaming, and production data pipelines. It is suited to an engineer with scalable systems experience across data, machine learning, or backend development.