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 disaster-recovery plan including backup automation, fail-over procedures and restore drills
Drive adoption of best practices and new features on the Databricks platform
Qualifications
At least 6 years of experience in each of the following: 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
At least 2 years of experience in each of the following: direct collaboration with Data Scientists or Machine Learning Engineers, IAM/RBAC, SRE and observability platforms (NewRelic, Data Dog, etc.), Python
Excellent written and verbal communication skills
Ability to work independently and as part of a team
Advantages
Experience with 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
Skills Required
- At least 6 years of experience in managing complex cloud platforms (Databricks, Snowflake, AWS, GCP, Azure, etc.)
- At least 6 years of experience in IaC tools (Databricks Asset Bundles, Terraform, Cloud Formation, etc.)
- At least 2 years of experience collaborating with Data Scientists or Machine Learning Engineers
- At least 2 years of experience in IAM/RBAC, SRE and observability platforms (NewRelic, Data Dog, etc.)
- At least 2 years of experience in Python
What We Do
QuantHealth is an AI company conducting patient-centric drug simulations to accelerate and de-risk drug development. Over 90% of drugs in clinical development stage fail to reach the market, which accumulates to a $45B/year direct lost to the pharma and biotech industry. Our platform allows our pharma and biotech partners to rapidly run thousands of variations of their clinical trials to optimize the trial design and significantly increase the probability of trial success, all while enabling discovery of new clinical opportunities and optimization strategies. QuantHealth has one the largest integrated datasets that spans the clinical, pharmacological and biological domains together with a proprietary AI platform that can predict patient-response to both approved and novel therapies.
