Dir , Commercial Data Capability

Job description

Job Description Director Commercial Data Capability - Head of Platform & Engineering Excellence We are seeking a bold, forward-thinking, and technically grounded leader to join our company Digital Human Health (DHH) organization and play a pivotal role in shaping the future of our commercial data capabilities. This role is the "Head of Platform & Engineering Excellence." This leader and their team provide the foundational platforms, tools, and services (quality, ops, GenAI infra) that Core data domains/solutions and the Emerging business solutions teams will consume. This leader serves as the primary engineering and platform partner to Core Data Domains and Emerging Business Solutions teams, providing the foundational capabilities they need to build and scale their respective data products. This leader will lead four specialized teams — Data Quality & Business Rules Management, Innovation Capabilities, Data Operations, and Foundational Capabilities — with a mandate to modernize our data infrastructure, embed Generative AI solutions across the commercial data organization, and ensure the long-term resilience and sustainability of our data platforms. Additionally, This leader will serve as the organization's champion for Data Architecture & Engineering excellence and embed AI assisted data product development as the standard for how we operate. This leader will lead the team that builds the foundational GenAI infrastructure (the "factory"), while the Core and Emerging business solutions teams use that factory to build specific GenAI-powered data products. Position Responsibilities Role Overview Serve as the champion for Data Architecture & Engineering excellence, leading the modernization of data infrastructure, embedding GenAI solutions, and ensuring the resilience of commercial data platforms to support the DHH organization. A. Team & Capability Leadership: 1. Lead Specialized Teams: Direct and oversee four core capability areas: Data Quality & Business Rules Management, Innovation Capabilities, Data Operations, and Foundational Capabilities. 2. Roadmap Execution: Translate the organization's GenAI and modernization ambitions into prioritized, executable technical and engineering roadmaps. 3. Role Model Leadership: Demonstrate senior leadership behaviors including cross-functional leadership, swift decision-making, strategic thinking, and fostering a culture of technical innovation. B. GenAI & Innovation Infrastructure: 1. GenAI-Ready Architecture: Architect and oversee the design of GenAI-ready data infrastructure, including curated semantic layers, vector store implementations, and Retrieval-Augmented Generation (RAG) pipelines. 2. Pilot to Production: Lead the Innovation Capabilities team in evaluating, piloting, and productionizing GenAI solutions to increase enterprise GenAI fluency and adoption. C. Foundational Architecture & Modernization: 1. Legacy-to-Modern Migration: Direct the Foundational Capabilities team in executing legacy-to-modern platform migrations and building future-proof, scalable architectural foundations. 2. Architecture Standards: Champion and enforce enterprise data architecture standards, including cloud-native design patterns, composable data product architecture, and API-first data access. D. Data Quality & Reliability (DataOps) 1. Data Operations Lifecycle: Lead the Data Operations team to ensure steady-state platform operations, high availability, rapid incident response, and strict SLA adherence. 2. Automated Data Quality: Oversee the Data Quality & Business Rules Management team to implement and scale an automated Data Quality (DQ) framework across all data tiers. Behavioral Competencies A. Technical Translation & Communication: A1. Bridges the gap: Excellent interpersonal skills with a proven ability to translate complex GenAI, architectural, and engineering concepts into clear business value for non-technical commercial stakeholders. B. High-Performance Team Leadership: B1. Builds and empowers: Strong organizational and leadership skills with a track record of hiring, building, and mentoring specialized, high-performing technical teams (Data Ops, Quality, Innovation). B2. Decisive execution: Demonstrates swift decision-making and cross-functional leadership to keep infrastructure migrations and pilots on track. C. Strategic Innovation & Change Champion C1. Drives modernization: Acts as the organization's champion for modern data architecture standards, fostering a culture of innovation that embraces AI-assisted workflows. C2. Forward-looking resilience: Balances the excitement of piloting GenAI with the disciplined mindset required to maintain steady-state operations and SLA adherence. D. Architectural Vision: D1. Translates ambition to reality: Ability to take the enterprise's high-level GenAI and modernization ambitions and break them down into prioritized, executable technical roadmaps. Functional Competencies A. Generative AI & Semantic Infrastructure A1. AI Implementation: Proven, hands-on expertise in Generative AI strategy and implementation, including direct experience with LLMs, RAG architectures, and vector databases in production environments. A2. AI-Ready Data: Deep understanding of preparing enterprise data for AI consumption (curated semantic modeling, metadata enrichment, ontology design, LangChain, Azure OpenAI, AWS Bedrock). B. Modern Data Architecture B1. Cloud-native design: Strong expertise in cloud-native data platforms, data lakehouse architectures, API-first data access, and composable data product architecture (prior exposure to Data Mesh operating models preferred). C. Platform Operations (DataOps) & Migration C1. Reliability at scale: Solid understanding of modern DataOps practices, including SLA/SLO management, incident response, and system observability. C2. Legacy modernization: Extensive experience directing legacy-to-modern platform migrations and building future-proof architectural foundations. D. Data Quality & Automated Governance D1. Automated frameworks: Experience leading large-scale data quality programs, embedding automated Business Rules Management, and implementing scalable DQ frameworks across all data tiers. D2. Responsible AI: Familiarity with implementing AI governance and responsible AI guardrails within the data infrastructure. Education: B.S. or M.S. in Engineering or related field (Business, Analytics, Engineering, Pharmacy, Technology fields, Liberal arts, Computer Science, Engineering, Data Science, etc.) Required: Minimum 10+ years of relevant work experience with a demonstrated track record in data architecture, data platform engineering, or data product leadership, with meaningful experience working in the biopharma or healthcare industry Preferred: Hands-on experience with specific GenAI and ML platform tooling such as Azure OpenAI Service, AWS Bedrock, Google Vertex AI, LangChain, LlamaIndex, or equivalent LLM orchestration frameworks Experience implementing AI governance and responsible AI frameworks, including model transparency, bias assessment, data lineage for AI pipelines, and audit-readiness for AI-generated outputs in regulated industries Familiarity with commercial biopharma data ecosystems and the specific challenges of preparing pharma commercial data (HCP data, promotional content, patient-linked data) for GenAI consumption Experience building or managing vector store implementations and embedding pipelines (e.g., Pinecone, Weaviate, pgvector, or equivalent) in a production enterprise environment Prior exposure to data mesh or data product operating model frameworks and experience translating those principles into practical platform and governance decisions Required Skills: AI Architecture, Business Intelligence (BI), Data Architecture Development, Database Administration, Data Engineering, Data Infrastructure, Data Lineage, Data Management, Data Modeling, DataOps, Data Quality, Data Quality Test