Director - Engineering, Forward-Deployed Engineering and Customer Outcomes

Excerpt from the original posting

Customer Success at Innovaccer 
Our mission is to turn our customers into tech-savvy superheroes, ensuring they achieve success using our platform to meet their organization’s business goals. If you're passionate about helping customers realize the value they seek with technology, then our customer success team is the right place for you.

About the Role
As the Director of Engineering, FDE and Customer Outcomes, you will lead an elite organization of multiple cross-functional engineering teams, each dedicated to a portfolio of tier-one healthcare customers. You are ultimately accountable for the technical delivery, enterprise configuration, and operational success of our AI-driven platform within complex healthcare ecosystems. 
Each team under your leadership operates with high autonomy, directly owning their customer delivery plans, data quality standards, and clinical/business outcomes. You will mentor engineering managers and technical leads across a global footprint to architect robust data ingestion pipelines, configure complex enterprise software, and deploy production-grade, compliant AI agents into live healthcare environments.

A Day in the Life 

Global Team Leadership & Delivery Ownership 
● Scale and Govern Multiple Teams: Hire, mentor, and scale multiple customer-facing engineering teams across regions, optimizing global resource allocation across health systems, payers, and life sciences clients. 
● Delivery Plan Accountability: Empower autonomous teams to own, track, and execute comprehensive implementation delivery plans, navigating critical paths from discovery to live production. 
● Customer Outcome Management: Focus engineering execution on direct business and clinical metrics (reducing administrative burden, improving workflow efficiency) to drive customer retention and high NPS/CSAT. 
● Quality Assurance & Standards: Establish baseline organizational standards for data integrity, product quality, architectural reviews, and formal customer sign-off criteria. 

Data Ingestion & Data Quality 
● Clinical Data Ingestion: Guide teams in building high-throughput pipelines to ingest structured and unstructured datasets (EHR notes, lab results, imaging metadata, claims) via HL7, FHIR, and DICOM. 
● Data Quality & Normalization: Standardize validation frameworks to sanitize, deduplicate, and normalize raw, heterogeneous healthcare data for downstream AI/ML consumption. 
● Deterministic Guardrails: Implement automated data-quality testing and sch…

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