QuantHealth
AI startup in the clinical trial space
SeniorInfrastructureEngineer(Databricks)
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Infrastructure Engineer (Databricks) at QuantHealth. Skills: Databricks infrastructure management, IaC, CI/CD pipelines, IAM model definition, Databricks best practices. manage Databricks infrastructure with IaC. build robust CI/CD pipelines”
What You'll Achieve.
simulate and optimize clinical trials; reduce development risk and cost; shorten timelines; improve the probability of clinical trial success; facilitate secure access for both human users and service principals to compute and data
Industry & Context.
What They're Looking For.
Must Have
DevOps, managing complex cloud platforms (Databricks, Snowflake, AWS, GCP, Azure, etc. ), IaC tools (Databricks Asset Bundles, Terraform, Cloud Formation, etc. ), Relational DB Management, multi-tenant and single-tenant deployment models, direct collaboration with Data Scientists or Machine Learning Engineers, IAM/RBAC, SRE and observability platforms (NewRelic, Data Dog, etc. ), Python
Nice to Have
Databricks: DABs, Unity Catalog, access control, metastores, jobs, apps, serving endpoints, clusters, compute policies, the Databricks Terraform provider, etc., Databricks certifications, Prior experience in the life sciences industry, Experience developing single-tenant solutions for large enterprise clients, Working on or closely alongside Data Engineering teams and with data warehouses, data lakes, etc., Working knowledge of PySpark, Spark, distributed computing
What You'll Do.
manage Databricks infrastructure with IaC
build robust CI/CD pipelines
define Databricks IAM model
implement and own flexible CICD tooling
design and enforce a robust data-isolation strategy on Unity Catalog
design a company-wide IAM model
manage and monitor Databricks costs
implement observability functionality
create a disaster-recovery plan
How You'll Work.
Team & Collaboration
partner with Data Engineers and Data Scientists; Work closely with Data Engineering, Platform Engineering and Data Science teams; direct collaboration with Data Scientists or Machine Learning Engineers; Working on or closely alongside Data Engineering teams
Communication Scope
Excellent written and verbal communication skills
Full Job Description
About QuantHealth QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies. Our platform helps customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success. About the Role Quant Health’s clinical trial simulations platform is composed of hundreds of Spark ETL jobs, several apps for use by internal domain experts, many streaming model inference endpoints and terabytes of patient, drug compound and clinical trial data. All of this is powered by Databricks and hosted across multiple environments in both multi-tenant and single-tenant deployment models. As Quant Health’s first infrastructure engineer focused on our internal machine learning platform, you’ll partner with Data Engineers and Data Scientists to manage our Databricks infrastructure with IaC, build robust CI/CD pipelines, define our Databricks IAM model and be the company’s foremost technical champion of Databricks best practices. Responsibilities Work closely with Data Engineering, Platform Engineering and Data Science teams to understand their work and the associated infrastructure requirements Architect, implement and own flexible CICD tooling that teams developing on Databricks will use to easily deploy their workloads Design and enforce a robust data-isolation strategy on Unity Catalog Design a company-wide IAM model to facilitate secure access for both human users and service principals to compute and data across all of Quant Health’s Databricks environments Manage and monitor Databricks costs, including cluster optimization, resource tagging strategy, cost-based alerting, etc. Implement observability functionality for our Data Science and Data Engineering workloads, including cross-component tracing, incident management, alerting and reliability metrics Create a disast
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