Senior Data Platform Engineer

CareMessage is a social enterprise and patient engagement platform focused on improving health equity for low-income populations in the United States. The Senior Data Platform Engineer will own the Google Cloud data infrastructure, ensuring reliable pipelines, secure patient information, and production-ready data workloads. The role also enables Data Science and Analysis teams through infrastructure automation, monitoring, CI/CD, and self-service data environments.

Responsibilities:

• Manage BigQuery datasets, Datastream, Cloud Run functions and jobs, service accounts and IAM for our data projects in Terraform, using our Infrastructure team's modules and standards
• Build and run CI/CD for data workloads, so every dataset, pipeline and function reaches production through code review rather than the console
• Set up monitoring, logging and alerting for freshness and failures, and fix root causes rather than symptoms
• Keep staging and production data environments consistent and reproducible from code
• Own PHI classification, column-level policy tags and masked views, applied automatically whenever a dataset is created or migrated
• Apply least-privilege IAM and secure defaults, such as internal-only ingress, to every data service we deploy
• Run automated checks that detect untagged PHI columns, drifted permissions or publicly reachable services, and alert before a person has to find them
• Partner with our Infrastructure and Compliance teams on access reviews, audits and migration runbooks
• Provide the organization with safe, self-serve environments to explore and build, with masked data and a clear path from a notebook or query to a scheduled production job
• Move the data team work into production with them, including the diabetes outcomes reporting our 2026 plan is measured on
• Help the Data team take their work from analysis to reliable, scheduled production jobs
• Remove infrastructure bottlenecks for the Data team, so they can spend their time on analysis instead of upkeep
• Document the platform with runbooks, definitions and lineage, so anyone on the team can use and trust it
• Ship your first infrastructure change through Terraform and code review
• Write a current-state map of our data infrastructure: ingestion paths, datasets, functions and jobs, service accounts, and what is and is not in code
• Take ownership of the open data-infrastructure and data-protection action items, each with a committed date
• Have BigQuery dataset creation, including PHI policy tags, running through Terraform and CI/CD
• Have every data Cloud Run function and job deploying through CI/CD with internal-only ingress and least-privilege service accounts
• Have agreed the top platform blockers with our Data Scientist and Data Analyst, and cleared the first of them
• Own the data platform and the triage of the Data team's infrastructure backlog
• Have scheduled drift checks for PHI tags, IAM and ingress, with alerting
• Have our Data Scientist and Data Analyst shipping scheduled work to production without waiting on infrastructure, starting with diabetes outcomes reporting v1
• 100% masking coverage on PHI columns as the warehouse grows, with any gap detected and closed automatically, and zero publicly reachable data services
• 100% of data infrastructure changes deployed through Terraform and CI/CD rather than by hand
• Median time for any internal team to get an approved data request (a new pipeline, dataset, report or access change) running in production, measured monthly against a baseline set in your first 30 days

Requirements:

• You have deep, hands-on expertise in Google Cloud for data infrastructure
• You have run BigQuery, IAM and service accounts, and serverless compute such as Cloud Run in production, and you understand how they fail
• You manage infrastructure as code and ship through CI/CD
• You have used Terraform or an equivalent, with pipelines such as GitHub Actions, to deploy data infrastructure, and you do not make production changes by hand
• You have owned a production data platform end to end: ingestion, including change data capture from an operational database, a layered warehouse, and the monitoring that tells you when it breaks
• You write production-quality SQL and Python
• You have protected sensitive data with controls defined in code, such as least-privilege IAM, column-level security or policy tags, and automated checks that catch when a control drifts
• You make the people who use your platform faster
• You have worked closely with data scientists or analysts, and you can point to a time you removed an infrastructure bottleneck that was slowing their work
• You are committed to health equity
• You want your engineering to serve people from low-income populations, and you can explain how reliable, protected data connects to that work
• You will be expected to apply that lens to your decisions, not just endorse it