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CareMessage

Mobile text messaging for patient engagement

Senior Data Platform Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USA$159.3k /yrRemote

About the Job

Requirements

  • Hands-on production expertise in Google Cloud data infrastructure, including BigQuery, IAM, service accounts, and serverless compute such as Cloud Run.
  • Experience managing infrastructure as code using Terraform or an equivalent and deploying through CI/CD pipelines such as GitHub Actions; production changes should not be made by hand.
  • Experience owning a production data platform end to end, including ingestion, change data capture from an operational database, a layered warehouse, and monitoring; production-quality SQL and Python skills.
  • Experience protecting sensitive data through controls defined in code, such as least-privilege IAM, column-level security or policy tags, and automated drift checks.
  • Experience working closely with data scientists or analysts and removing infrastructure bottlenecks that slow their work.
  • Commitment to health equity and ability to explain how reliable, protected data supports that goal.

Responsibilities

  • Manage BigQuery datasets, Datastream, Cloud Run functions and jobs, service accounts, and IAM for data projects in Terraform using the Infrastructure team's modules and standards.
  • Build and run CI/CD for data workloads so datasets, pipelines, and functions reach production through code review rather than the console.
  • Set up monitoring, logging, and alerting for data freshness and failures, and fix root causes.
  • Keep staging and production data environments consistent and reproducible from code.
  • Own PHI classification, column-level policy tags, and masked views, applying them automatically whenever a dataset is created or migrated.
  • Apply least-privilege IAM and secure defaults, such as internal-only ingress, to each deployed data service.
  • Run automated checks for untagged PHI columns, permission drift, or publicly reachable services, with alerts before manual detection is needed.
  • Partner with Infrastructure and Compliance teams on access reviews, audits, and migration runbooks.
  • Provide safe, self-serve environments for exploration and development, with masked data and a clear path from notebooks or queries to scheduled production jobs.
  • Move the Data team's work into production, including diabetes outcomes reporting in the 2026 plan.
  • Help the Data team move work from analysis to reliable, scheduled production jobs.
  • Remove infrastructure bottlenecks so the Data team can focus on analysis rather than upkeep.
  • Document the platform with runbooks, definitions, and lineage so the team can use and trust it.
  • Ship an initial infrastructure change through Terraform and code review within the first 30 days.
  • Create a current-state map of data infrastructure, including ingestion paths, datasets, functions and jobs, service accounts, and what is or is not managed in code.
  • Take ownership of open data-infrastructure and data-protection action items, each with a committed date.
  • Within 60 days, manage BigQuery dataset creation, including PHI policy tags, through Terraform and CI/CD.
  • Within 60 days, deploy every data Cloud Run function and job through CI/CD with internal-only ingress and least-privilege service accounts.
  • Within 60 days, agree on the top platform blockers with the Data Scientist and Data Analyst and clear the first blocker.
  • Within 90 days, own the data platform and triage the Data team's infrastructure backlog.
  • Within 90 days, schedule drift checks for PHI tags, IAM, and ingress, with alerting.
  • Within 90 days, enable the Data Scientist and Data Analyst to ship scheduled work to production without waiting on infrastructure, beginning with diabetes outcomes reporting v1.
  • Maintain 100% masking coverage on PHI columns as the warehouse grows, automatically detecting and closing gaps, and ensure zero data services are publicly reachable.
  • Ensure 100% of data infrastructure changes are deployed through Terraform and CI/CD rather than manually.
  • Track monthly median time for an internal team to get an approved data request running in production against a baseline set in the first 30 days.

About the Company

CareMessage

Mobile text messaging for patient engagement

CareMessage helps safety-net healthcare providers improve patient health through mobile text messaging. It provides a platform that clinics and community health centers can use to reach patients with reminders, health education, and support for chronic disease management. The system works by sending automated SMS messages to patients and organizing outreach through a simple interface that clinicians can use without technical expertise. Unlike general health-tech companies, CareMessage focuses on underserved populations and on providers serving low-income communities, offering a subscription plan priced by patient count and feature use. This model creates a steady revenue stream for the company while letting clinics scale their outreach as their patient base grows. The goal is to raise health outcomes for underserved patients by making it easy for providers to engage patients and deliver timely health information.

Company size
51-200
Company stage
Grant
Total funding
$19.4M
Headquarters
San Francisco, CA
Founded
2012

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