Senior Data Platform Engineer

New

JobgetherHealthcare data

Based in United StatesFull-TimeSenior

Salary159,319 USD per year

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Job Details

Have deep, hands-on production experience with Google Cloud data infrastructure, including BigQuery, IAM, service accounts, and Cloud Run.
Have experience managing infrastructure as code with Terraform or an equivalent technology.
Have experience with CI/CD platforms such as GitHub Actions.
Have experience owning or operating a production data platform end to end, including ingestion, change data capture, layered data warehouses, monitoring, and operational reliability.
Have strong production-level SQL and Python skills for building and maintaining reliable data workloads.
Have experience securing sensitive or regulated data using least-privilege IAM, column-level security, policy tags, masked views, and automated security checks.
Have experience working with data scientists, analysts, or other data-focused teams to remove infrastructure constraints.
Understand cloud infrastructure and data services in production, including troubleshooting failures and addressing root causes.
Be able to establish reliable practices for deployment, monitoring, documentation, and operational ownership.
Communicate and collaborate across engineering, data, infrastructure, security, and compliance teams.
Be able to operate independently, prioritize competing needs, and make thoughtful technical decisions.

Own the Google Cloud data platform, including BigQuery, Datastream, Cloud Run functions and jobs, service accounts, IAM, and related infrastructure.
Manage data infrastructure through Terraform and deliver production changes through code review and CI/CD.
Build and maintain CI/CD pipelines for datasets, data pipelines, and serverless workloads.
Establish monitoring, logging, alerting, and automated drift detection for data freshness, infrastructure failures, PHI controls, IAM permissions, and service exposure.
Implement secure-by-default controls for sensitive patient information, including PHI classification, policy tags, masked views, least-privilege access, and internal-only service ingress.
Partner with infrastructure and compliance teams on access reviews, audits, security controls, migration planning, and operational runbooks.
Provide safe self-service environments and paths from exploratory analysis to scheduled production workloads for data scientists and analysts.
Help move analytical initiatives, including outcomes reporting and other data workloads, into production.
Own the data team's infrastructure backlog, remove platform bottlenecks, and provide technical guidance.
Maintain documentation covering platform architecture, runbooks, data definitions, lineage, and operational procedures.

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