Senior Infrastructure Engineer (Databricks)

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