Principal Engineer, Data

As Principal Data Engineer, you will serve as Ensemble’s senior technical authority on data architecture — a high‑level individual contributor, not a people manager. You will assess data engineering practices across the organization, architect solutions that scale across teams, and establish a strong foundation for AI ‑ driven capabilities across the organization.

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 data engineering 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 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:

Technical Decision Making - Makes sound architectural and technical decisions by gathering and weighing 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, providing design-review feedback, and modeling sound engineering judgment; 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).

Technical Program Ownership - Demonstrates high accountability for architectural initiatives and proofs of concept from inception through adoption; 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.

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.

Technical Assessment & Architecture

• 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.
• 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.
• 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 and design thinking, elevating their capability without holding formal management responsibility for their performance.
• 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.

Knowledge, Skills and Abilities

• 10+ years of relevant job 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
• Experience designing data platforms that support AI/ML, advanced analytics, or intelligent automation workloads.
• Deep experience with Azure cloud services and Databricks, including Spark‑based data engineering patterns.
• Proven ability to translate business needs into scalable data solutions.
• Experience owning or operating large‑scale cloud data platforms, including responsibility for cost optimization, performance tuning, and platform reliability.
• Demonstrated ability to scale data engineering through enablement, standards, and platforms, not just direct delivery.
• Strong understanding of Databricks pricing models, workload optimization, and Azure cloud cost drivers preferred .
• Demonstrated experience assessing data engineering practices across multiple teams or business units and translating findings into a concrete architectural or technology roadmap.
• Track record of evaluating emerging tools and technologies for real fit within an organization, and driving adoption decisions through credibility and clear communication rather than formal authority.
• 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.

• This position pays between $148,800 – $256,700 based on experience

• Must be inquisitive and demonstrate openness to innovation including AI to explore better processes and ways to alleviate friction and improve patient and client experiences.

• This is a remote position; however, candidates must be willing and able to travel to and work onsite at client, temporary, or corporate office locations as business needs require.

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