Sr Platform Engineer, Data

Thank you for considering a career at Ensemble!

Ensemble is a leading provider of technology-enabled revenue cycle management solutions for health systems, including hospitals and affiliated physician groups. They offer end-to-end revenue cycle solutions as well as a comprehensive suite of point solutions to clients across the country.

Ensemble keeps communities healthy by keeping hospitals healthy. We recognize that healthcare requires a human touch, and we believe that every touch should be meaningful. This is why our people are the most important part of who we are. By empowering them to challenge the status quo, we know they will be the difference!

O.N.E Purpose:

Customer Obsession: Consistently provide exceptional experiences for our clients, patients, and colleagues by understanding their needs and exceeding their expectations.

Embracing New Ideas: Continuously innovate by embracing emerging technology and fostering a culture of creativity and experimentation.

Striving for Excellence: Execute at a high level by demonstrating our “Best in KLAS” Ensemble Difference Principles and consistently delivering outstanding results.

The Opportunity:

JOB SUMMARY

By embodying our core purpose of customer obsession, new ideas, and driving innovation, you will help ensure that every touchpoint is meaningful and contributes to our mission of redefining the possible in healthcare.

As a Senior Platform Engineer, you are a hands-on technical engineer who sits at the intersection of data engineering, cloud platform, and networking — building the infrastructure and pipelines that make Ensemble's data platform reliable, secure, and scalable. You will design and implement Azure-based platform solutions, author data pipelines, harden network architecture, and apply solid software engineering discipline across everything you ship.

This is primarily a delivery role. You will spend the majority of your time writing code, configuring infrastructure, reviewing pull requests, and solving hard operational problems — not managing people or setting org-wide strategy. You bring strong opinions about platform reliability, cloud cost, and network security, and you translate those opinions into running systems.

We are open to strong cloud/platform engineers who want to go deep on data, and to strong data engineers who have grown into platform and networking responsibility. What matters is that you have done hard, hands-on work at the intersection of these disciplines — you understand both why a pipeline fails and why a Private Endpoint DNS record is wrong, and you are comfortable owning both.

JOB COMPETENCIES

Platform & Cloud Engineering — Designs, builds, and operates Azure-based platform infrastructure — compute, storage, networking, identity, and security — with a bias toward infrastructure as code, repeatability, and operational simplicity; balances speed of delivery with platform reliability and cost efficiency.

Data Engineering Depth — Delivers scalable, well-tested data pipelines and transformations on Databricks and Azure Data Factory; applies sound data modeling, performance tuning, and data quality discipline as standard practice rather than afterthoughts.

Network & Security Awareness — Understands Azure networking constructs — VNets, subnets, NSGs, Private Endpoints, DNS, and ExpressRoute/VPN — and applies them correctly in platform design; proactively identifies and closes network attack surface without creating unnecessary friction for engineering teams.

Software Engineering Craft — Writes clean, testable, maintainable code; participates actively in code and design reviews; treats CI/CD and automated testing as the default way of working; holds a high bar for version control hygiene and deployment safety.

Operational Ownership — Takes end-to-end accountability for the platform components and pipelines in scope — from initial design through production monitoring, incident response, and continuous improvement; does not hand off and walk away.

Technical Communication — Communicates clearly with both engineering peers and non-technical stakeholders; can explain complex networking or data platform trade-offs to a product manager or a VP without losing the audience or oversimplifying the substance.

Continuous Learning & AI Fluency — Stays current with the Azure, Databricks, and data engineering ecosystem; consistently applies generative AI tools to accelerate engineering work, improve code quality, and troubleshoot complex systems.

ESSENTIAL JOB FUNCTIONS

Customer Obsession, New Ideas, Striving for Excellence

Consistently deliver high-quality platform and data solutions that improve the reliability, speed, and accuracy of data reaching internal and external stakeholders.

Apply generative AI tools in day-to-day engineering work to accelerate development, improve code quality, troubleshoot complex systems, and design scalable solutions.

Data Pipeline & Platform Engineering

Design, build, test, deploy, monitor, and continuously improve scalable data pipelines using Azure Data Factory, Databricks, Apache Spark, and Python.

Implement Delta Lake patterns on Azure Data Lake Storage (ADLS) including table design, schema evolution, Z-ordering, VACUUM, and partition strategies.

Build and maintain event-driven integrations using Azure Service Bus, Event Hub, and Azure Functions to support real-time and near-real-time data flows.

Implement data quality monitoring frameworks — row counts, schema validation, null checks, freshness SLAs — as first-class pipeline components.

Develop reusable pipeline templates and platform abstractions that accelerate delivery across the data engineering team.

Translate product and business requirements into incremental, testable technical deliverables with clear acceptance criteria.

Azure Cloud Platform & Infrastructure

Provision and manage Azure infrastructure using infrastructure as code (Terraform, Bicep, or ARM templates), treating all platform configuration as version-controlled code.

Design and operate Azure-native services including Azure Data Factory, Azure Databricks, Azure Data Lake Storage Gen2, Microsoft Fabric (or Synapse), Azure Key Vault, and Azure Monitor.

Manage identity and access controls using Azure Active Directory, Managed Identities, RBAC, and Unity Catalog — applying least-privilege principles across all platform components.

Implement platform-level observability: pipeline SLAs, workload performance dashboards, alerting, and cost tracking via Azure Monitor, Log Analytics, and Databricks system tables.

Contribute to FinOps practices by monitoring compute costs, right-sizing clusters, configuring auto-termination policies, and flagging cost anomalies.

Support containerized workloads running on Azure Kubernetes Service (AKS) where data platform components require container-based deployment patterns.

Networking & Security

Design and implement secure Azure networking architectures for data platform workloads — VNets, subnets, NSGs, Private Endpoints, Private DNS Zones, and service endpoints — eliminating unnecessary public internet exposure for PaaS services.

Configure, troubleshoot, and document Private Link connectivity across Azure PaaS services (ADLS, Azure SQL, Key Vault, Event Hub, Databricks); diagnose DNS resolution issues and routing problems end-to-end.

Apply encryption-in-transit and encryption-at-rest standards; maintain TLS configurations, key rotation policies, and secret management practices in line with security baselines; participate in security reviews and threat modeling for new platform components.

Partner with Cloud and Infrastructure teams on hub-and-spoke or landing zone network topologies; configure and validate firewall rules and IP allowlisting in a way that balances security posture with engineering team productivity.

CI/CD, DevOps & Engineering Standards

Build and maintain CI/CD pipelines in Azure DevOps that automate t