• Partner with enterprise sales, pre-sales engineers, and delivery teams to define and design tailored service solutions (Professional and Managed Services) for large, complex customer environments • Lead the services discovery process, identifying customer objectives, technical requirements, and operational constraints • Architect comprehensive services proposals that include deployment, migration, integration, adoption, and lifecycle management components • Design Managed Services models that include monitoring, proactive maintenance, optimization, and ongoing technical management • Build detailed Statements of Work (SOWs), service definitions, and solution blueprints that align with customer expectations and internal delivery capabilities • Collaborate with Product Management and Operations to standardize and scale repeatable service offerings while maintaining flexibility for customization • Provide subject matter expertise during customer presentations, executive reviews, and proposal negotiations • Ensure service solutions are technically sound, financially viable, and operationally deliverable • Serve as a trusted advisor to enterprise customers, guiding them through service transformation and managed services adoption • Maintain a deep understanding of industry trends, frameworks (ITIL, ISO, etc.), and emerging technologies to ensure service innovation and competitive differentiation

• Architect end-to-end AWS-based solutions for Amazon Connect digital contact centers. • Build custom chat widgets and agent desktops using Lambda, API Gateway, and modern front-end frameworks. • Design event-driven, scalable cloud architectures following AWS Well-Architected principles. • Implement GenAI solutions using Amazon Bedrock, SageMaker, and third-party/open-source LLMs. • Integrate conversational intelligence into customer and agent workflows with AI/ML teams. • Configure and manage IAM, VPCs, CloudWatch monitoring, logging, and security controls. • Ensure compliance with enterprise security and governance standards. • Integrate Amazon Connect with third-party CRMs and enterprise systems. • Design streaming APIs, event-handling logic, and reporting APIs. • Lead cross-functional engineering, DevOps, and product teams to deliver complex solutions. • Align architectural strategy with business and customer experience goals.

• Join the Tracking & Integrations Engineering team within a global data-driven organization • Own the implementation and governance of the tracking architecture across websites and mobile applications • Ensure that user interactions are correctly captured, structured, and delivered to downstream analytics and data platforms for accurate reporting, experimentation, and marketing optimization

• Architect the Exchange Layer: Design and own the Common Data Models (CDMs) that serve as the universal language for scientific data across our customer base. Move the platform from bespoke, one-off mappings to a standardized "exchange layer" that ensures interoperability. • Empower Forward Deployed Engineering: Create the data contracts and standardized definitions that FDEs rely on. Your models will be the toolkit that allows them to deploy faster and with higher confidence, knowing they are building on a stable, consistent semantic foundation. • Standardization vs. Flexibility: Strike the strategic balance between rigid global standards (for cross-customer exchange) and local flexibility. Define the core "immutable" aspects of the model versus where extension is permitted. • The "Forest" – Business Alignment: Translate high-level business goals (e.g., "accelerate time-to-insight for biologics") into concrete data modeling strategies. Ensure our semantic roadmap directly supports the scientific questions our customers—and our internal teams—need to answer. • The "Trees" – Hands-on Modeling: Roll up your sleeves to design and implement complex ontologies and taxonomies. Model intricate scientific relationships (e.g., linking a "Cell Line" in an ELN to "Flow Cytometry Results") with precision. • Software & Data Engineering Integration: Work directly with Engineering to architect the software systems that consume these models. Ensure that the "perfect" ontology does not break query performance or system scalability. • Data Contracts & Governance: Establish the "rules of the road" for data quality and consistency. Define how data contracts are versioned, enforced, and evolved, ensuring that downstream consumers (AI teams, FDEs, Scientists) can trust the data structure. • Scientific Translation: Partner with Scientific Business Analysts to decode the complexity of biopharma R&D. Turn ambiguous scientific requirements into rigorous, machine-readable data structures. • Interoperability: Architect models that ensure our data is FAIR (Findable, Accessible, Interoperable, Reusable) and ready for downstream AI/ML applications.