AVP, Data Engineering (Enablement)

Ensemble Health Partners•Work at Home - Tennessee - Other, TN

20h

About The Position

As the AVP of Data Engineering (Enablement), you will own and evolve Ensemble’s enterprise data platform and data product strategy to enable analytics at scale and establish a strong foundation for AI‑driven capabilities across the organization. You will lead the design and execution of 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. This role is responsible for translating business and product strategy into scalable data platform capabilities — ensuring data is AI‑ready, secure, observable, cost‑efficient, and consumable by analytics, product, and machine learning teams. Success in this role will be measured by platform adoption, data trust, delivery velocity, and the organization’s readiness to operationalize AI.

Requirements

10 or more years of professionally related experience
Masters Degree or Equivalent Experience
10+ years of coding experience with ANSI SQL.
3+ years working with big data technologies including but not limited to Databricks, SPARK, Azure, Power BI, 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.
Experience operating data platforms in regulated or healthcare environments (HIPAA, PHI, data privacy) strongly preferred.
Proven ability to translate business and clinical domain needs into scalable data solutions.
Makes day-to-day leadership decisions by securing and comparing information from multiple sources to identify issues; commits to an action after weighing alternative solutions against important criteria; effectively communicates decisions to the appropriate people and teams and holds them accountable.
Achieves results through other leaders by empowering them and providing feedback, instruction and development (coaching the coach) to develop their own associates; plans and supports the growth of individual skills and abilities in preparation for their next role (building bench); focuses on retention of high performers.
Successfully shares authority and responsibilities with others to move decision making and accountability downward through the organization while accomplishing strategic priorities; maintains personal ownership of outcomes without excessive involvement.
Inspires and sustains team unity and engagement by developing, motivating, and guiding the team to achieve results together through productive relationships and work.
Clearly and succinctly conveys information and ideas; communicates in a focused and compelling way that captures and holds others’ attention (appropriate, impactful, and clear).
Demonstrates high accountability and responsibility for projects and programs from inception through completion/implementation; manages budget and resource planning and awareness to ensure maximized output, reduced waste and exceptional results.

Nice To Haves

Experience operating data platforms in regulated or healthcare environments (HIPAA, PHI, data privacy) strongly preferred.

Responsibilities

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, schema stability, timeliness, and explainability requirements.
Treat enterprise datasets as data products, with clear ownership, quality guarantees, documentation, and usage metrics.
Build and evangelize a reusable data product layer that enables self-service analytics and accelerates downstream innovation.
Define platform-level KPIs (adoption, reliability, cost efficiency, time-to-data) and continuously improve based on measurable outcomes.
Implement governance-by-design, including data lineage, access controls, privacy protections, and auditability across the data platform.
Ensure data pipelines and data products meet healthcare regulatory and compliance expectations, with strong controls around PHI and sensitive data.
Champion data observability, including data quality monitoring, freshness SLAs, pipeline reliability, and cost transparency.