ConvergeHEALTH - Forward Deployed Engineer, Data Studio-Supply Chain - Innovation_Delivery_Transformation

The Team

This role sits within Converge for Healthcare, Deloitte's industry-focused asset studio for healthcare, part of Deloitte Consulting's Innovation & Delivery Transformation (I&DT) practice. I&DT brings an engineering- and innovation-led mindset to how Deloitte builds, delivers, and scales technology-enabled solutions.

Data Studio is Converge for Healthcare's foundational platform — combining agentic AI and analytics capabilities atop a unified healthcare data foundation to turn healthcare data into decision-ready insight and automated action across the Converge for Healthcare product portfolio.

Forward Deployed Engineers operate at the intersection of client delivery, applied engineering, and product strategy — partnering with account and product teams pre-sale to qualify and shape opportunities, and with client and delivery teams post-sale to prove and accelerate agentic value, feeding real-world learnings back into the platform roadmap.

Position Summary

As a Forward Deployed Engineer within Converge for Healthcare, you will work directly with clients, Deloitte Consulting's broader Health Care consulting practice, and Converge for Healthcare's product and engineering teams. Together, you'll build out Data Studio's agentic use cases — starting from Deloitte's agentic and analytics platform and its unified healthcare data foundation — into production-scale capabilities in each client's environment, powering provider and payer performance improvement across financial, clinical, and operational domains.

This is a techno-functional role: it requires both deep technical skill and deep healthcare domain knowledge, brought together to adapt agentic capabilities to the specific workflows, data, and nuances of each client. You'll operate at the intersection of client delivery, applied engineering, and product feedback: engaging with clients and Health Care consulting teams both pre- and post-sale, and partnering with Converge for Healthcare's product and engineering teams to translate field learnings into reusable capabilities. The role draws on healthcare breadth rather than a single specialty, spanning provider and payer domains — from revenue cycle and clinical operations to network strategy, actuarial, and beyond.

Recruiting for this position ends 9/30/26.

Work you’ll do:

• Presales & solution shaping. Partner with Health Care consulting teams and Converge for Healthcare's account and product teams during the sales cycle — running technical discovery, demonstrations, and use-case fit assessments that qualify client needs and shape a credible path to production value.
• Agentic use case development. Work hand-in-hand with clients to build out agentic use cases on Data Studio — sometimes adapting and extending Deloitte's library of proven use cases, other times building greenfield to solve a client-specific problem — engineering toward durable, production-scale capabilities rather than one-off demonstrations.
• Data readiness for agentic use cases. Define the data and context each agentic use case depends on to perform reliably, confirm the unified data foundation can support it, and partner with data engineering and client teams to close any gaps — owning what the use case needs to work, not the underlying pipelines that feed the foundation.
• Client enablement & value realization. Support client training, onboarding, and adoption activities, and stay engaged post-launch to help identify expansion opportunities grounded in real usage.
• Field-to-product feedback. Capture recurring client needs, data patterns, and delivery learnings, and translate them into concrete agentic capability requests and reusable extensions for product and engineering teams.

The successful candidate would possess these skills:

• Genuine techno-functional profile — deep technical capability paired with deep healthcare domain fluency, applying both together to adapt agentic solutions to real client workflows
• Strong healthcare domain knowledge spanning provider and/or payer domains — such as revenue cycle, clinical operations, network strategy, or actuarial — with the versatility to apply it across multiple areas rather than a single specialty
• Strong hands-on engineering skills, including proficiency in Python and SQL and comfort working with APIs, data integration patterns, and cloud-based services (e.g., AWS) to build and deploy production-grade capabilities in client environments
• Hands-on experience designing, adapting, or hardening agentic workflows — multi-agent coordination, tool invocation, memory/state management — using modern agent frameworks, with the judgment to take a working use case to a production-grade client deployment
• Working knowledge of the retrieval and knowledge layer behind agentic systems — RAG pipelines, embeddings and vector stores, and increasingly knowledge graphs — including how to ground use cases in large volumes of unstructured healthcare data (e.g., clinical notes, payer policies, contracts) alongside structured sources
• Intellectual curiosity and a strong pull toward what's next — actively following how agentic frameworks, tooling, and techniques are evolving, and quick to adopt new approaches in a field where today's stack may look very different in six months
• Client-facing credibility to translate technical tradeoffs and constraints into clear business decisions, carrying both technical and executive conversations within the same engagement
• Judgment to size each engagement — recognizing when an agentic use case can be reused with light configuration, when it needs substantial extension, and when a client problem calls for building greenfield
• Solid understanding of healthcare data and data standards (e.g., claims, remittances, EMR data, HL7, FHIR, X12 EDI), with the ability to reason through data quality and business context
• Comfort supporting commercial and sales-adjacent activities such as demos, use-case fit assessments, and solution qualification, alongside post-sale delivery and value realization work
• Ability to build trust and durable working relationships quickly with client stakeholders and account teams in predominantly virtual and distributed delivery environments
• Excellent written and verbal communication skills, including the ability to develop client-facing materials, lead technical discovery conversations, and work effectively across distributed teams and international time zones

Qualifications

Required:

• Bachelor's degree in Computer Science, Information Systems, Engineering, Health Informatics, or a related technical discipline
• 4+ years of experience in software engineering, solution deployment, data engineering, or client-facing technical delivery roles
• 2+ years of experience with SQL and/or Python
• Ability to travel up to 25%, on average, based on the work you do and the clients and industries/sectors you serve.
• Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future

Preferred:

• Master's degree in Computer Science, Engineering, Information Systems, or a related technical discipline

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $105,400-$207,800.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.