Principal Software Architect- Data Platform, 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:

As Principal Software Architect, you will serve as Ensemble’s senior technical authority on data platform engineering — a high‑level individual contributor, not a people manager. This is a software engineering role first: you will bring professional software architecture discipline — design patterns, modularity, repo structure, and system design — to a platform built on Databricks and Azure. You will re‑envision our architectural patterns, assess engineering practices across the organization, and establish a strong foundation for AI‑driven capabilities across the organization.

Much of our data platform work is heavy software engineering: services and pipelines that live in Git repositories, move through code review, and are built, tested, and deployed like any other software system. You will architect and design a cloud‑native data platform built on Azure and Databricks, enabling trusted, governed, and reusable data products that accelerate insight generation, operational automation, and emerging AI use cases. You will also own the organization’s technology roadmap, staying ahead of emerging tools and techniques and recommending what we should evaluate or adopt next.

This role serves as a technical escalation point and architectural authority for the Data Platform, influencing software design, repository and branching strategy, CI/CD practices, performance, scalability, cost optimization, developer experience, and long‑term sustainability of Ensemble’s data ecosystem through credibility and technical judgment rather than formal authority.

Job Competencies

Software Architecture & Technical Decision Making - Makes sound software architecture and design decisions, including system structure, design patterns, repository and service boundaries, and build-vs-buy trade-offs; gathers and weighs information from multiple sources, evaluating trade-offs against reliability, cost, and scalability; clearly communicates decisions and rationale to technical and non-technical stakeholders; drives results through the strength of the recommendation, not positional authority.

Technical Mentorship - Elevates the capability of senior and staff engineers by sharing architectural thinking, participating in code and design reviews, and modeling sound engineering judgment and software craftsmanship; develops others through example and coaching rather than formal supervision, without holding management responsibility for their performance or career progression.

Influencing Without Authority - Drives adoption of architectural standards and technical direction across teams through credibility, clear reasoning, and relationship-building rather than reporting lines; earns buy-in from engineering, product, and leadership stakeholders who do not report to this role.

Cross-Functional Collaboration - Builds productive working relationships across data engineering, analytics, data science, product, and platform teams to align architectural decisions with business goals, without formal authority over those teams.

Technical & Executive Communication - Clearly and succinctly conveys complex architectural and technical concepts to both engineering peers and executive stakeholders; communicates in a focused and compelling way that captures and holds others’ attention (appropriate, impactful, and clear).

Engineering Craftsmanship & Code Quality - Demonstrates high accountability for architectural initiatives and proofs of concept from inception through adoption; champions disciplined version control practices, automated testing, and CI/CD as the default way of working, not an afterthought; considers cost and resourcing impact to ensure maximized output, reduced waste, and exceptional results.

Innovation & Continuous Learning - Stays ahead of the industry by continuously evaluating emerging tools, frameworks, and techniques for genuine fit within the organization; runs proofs of concept to validate new approaches before recommending broader adoption, rather than chasing trends for their own sake.

Essential Job Functions

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.

Software Architecture & Platform Re-Envisioning

• Re-envision the data platform’s architectural patterns, retiring legacy approaches and replacing them with modern, modular system design.

• Conduct cross-organizational assessments of existing data pipelines, platforms, and engineering practices to identify inefficiencies, technical debt, risk, and opportunities for improvement.

• Architect scalable, secure, and cost-efficient data solutions that span multiple teams and business domains, built on Databricks and Azure.

• Define service and repository boundaries, interfaces, and design patterns that keep the platform modular, testable, and maintainable as it grows.

• Act as a technical escalation point for the organization’s hardest data engineering problems, diagnosing recurring or systemic reliability issues in tier-1 pipelines and datasets. Engineering Excellence: Version Control, CI/CD & Code Quality

• Define and evolve git workflows, branching strategies, and pull request standards that bring professional software engineering discipline to data platform repositories.

• Establish and champion CI/CD pipelines that automate testing, validation, and deployment of data pipelines and platform code.

• Drive adoption of automated testing practices — unit, integration, and data quality tests — as a core part of how the team builds and ships, not an afterthought.

• Define and evolve data engineering standards, patterns, and architectural guardrails used across engineering teams, balancing standardization with the flexibility teams need to move quickly. AI & Advanced Analytics Enablement

• Establish and evolve a data platform architecture that enables AI, machine learning, and advanced analytics workloads, including feature readiness, training data quality, lineage, and observability.

• Partner with Data Science, Analytics, Product, and Cloud teams to ensure data platforms support model development, deployment, and monitoring without creating operational or governance risk.

• Define and enforce AI-ready data standards, including data quality thresholds, metadata completeness, schema stability, and data timeliness required to support advanced analytics and AI workloads. Technology Roadmap & Innovation

• Stay ahead of the industry by continuously evaluating emerging tools, frameworks, and techniques in the data and AI space for genuine fit within Ensemble’s environment, rather than chasing trends for their own sake.

• Run proofs of concept and technical spikes to validate new approaches before recommending broader adoption.

• Author and maintain a data engineering technology roadmap aligned to business priorities, with clear reasoning on trade-offs, cost, and adoption sequencing.

• Present roadmap recommendations and their business impact to both technical and executive stakeholders, translating deep technical trade-offs into clear strategic choices. Platform Standards, Governance & Cost Optimization

• Treat enterprise datasets as data products, with clear ownership, quality guarantees, documentation, and usage metrics.

• Establish platform standards and architectural guardrails to optimize compute efficiency, storage utilization, query performance, and pipeline reliability.

• Partner with Finance, Cloud, and Engineering leaders to implement cost transparency (FinOps) across Databricks and Azure, including budgets, usage monitoring, and proactive optimization.

• Drive platform decisions using clear metrics such as cost-to-serve, workload efficiency, platform SLAs, and utilization trends.

• Ensure platform-level observability, including workload performance, cost drivers, usage patterns, and capacity forecasting. Cross-Functional Influence & Mentorship

• Mentor senior and staff engineers on architecture, system design, and software craftsmanship, elevating their capability without holding formal management responsibility for their performance.

• Lead code and design reviews that raise the engineering bar across the team.

• Influence technical direction across teams through credibility, clear communication, and sound technical judgment rather than positional authority.

• Partner with data engineering, analytics, data science, and platform/infrastructure teams to ensure architectural decisions stay aligned with business goals.

This document is not an exhaustive list of all responsibilities, skills, duties, requirements, or working conditions associated with the job. Associates may be required to perform other job related duties as required by their supervisor, subject to reasonable accommodation.

Other Preferred Knowledge, Skills and Abilities

• 10+ years of experience
• Deep experience designing and reviewing SQL‑based data models and transformations at enterprise scale.
• 3+ years working with big data technologies including but not limited to Databricks, SPARK, Azure, with a willingness and ability to learn new ones
• Excellent understanding of engineering fundamentals: testing automation, code reviews, telemetry, iterative delivery and DevOps
• Experience with polyglot storage architectures including relational, columnar, key-value, graph or equivalent
• Demonstrated ability to communicate effectively to both technical and non-technical, globally distributed audiences
• Solid foundations in formal architecture, design patterns and best practices
• Proven track record as a hands-on software architect, with deep knowledge of design patterns, modular system design, and service/repository boundaries.
• Expert-level proficiency with Git and modern version control workflows (trunk-based development, branching strategies, pull request and code review practices).
• Strong experience designing and implementing CI/CD pipelines and automated testing (unit, integration, and data quality tests) for software and data systems.
• Track record of leading significant architectural refactors or platform re-platforming efforts, measurably improving maintainability, reliability, or developer velocity.
• Experience designing data platforms that support AI/ML, advanced analytics, or intelligent automation workloads.
• Deep, hands-on experience with Databricks and Azure cloud services is strongly preferred, including Spark-based data engineering patterns.
• Experience operating data platfo