Associate Director / Director, Data & AI Solution Development

Kardigan•Princeton, NJ

About The Position

Kardigan is a heart health company developing targeted treatments for cardiovascular disease. Its Information Technology team partners across the enterprise to deliver secure, reliable technology solutions that accelerate execution and enable the use of data and artificial intelligence. The team emphasizes collaboration, ownership, continuous improvement, and curiosity. The Associate Director / Director, Data & AI Solution Development is the solution-design leader for Kardigan’s enterprise data and AI solutions. The role translates business, scientific, and operational needs into solution architectures, data models, integration patterns, and requirements for initiatives involving data products, enterprise data management, and AI-enabled applications. It focuses on making enterprise data accurate, governed, discoverable, and useful as context for people and AI systems, including LLMs, RAG systems, analytics copilots, and other AI/ML applications. The role primarily serves as a solution architect and design authority, setting architecture standards, leading requirements analysis and design workshops, and working with business and scientific stakeholders. It partners with the Solution Delivery team, which owns solution build and deployment, and Data Platform Engineering, which owns platform build and operations. The position reports to the Executive Director overseeing Data, AI & Technology and is a strategic peer to Data Science/Analytics and AI leadership. The position leads enterprise data and AI solution architecture; defines reusable patterns and requirements; shapes the solutions roadmap and intake process; and establishes requirements for data platforms, APIs, semantic layers, governance, data products, AI/ML workloads, and AI-ready data foundations. It also defines quality, adoption, value, and design effectiveness measures; supports regulatory, privacy, security, and responsible-AI requirements; and collaborates with IT, cybersecurity, platform, delivery, and enterprise architecture peers to ensure designs are coherent and secure.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related quantitative discipline.
Proven track record designing enterprise-scale data and AI solutions, including solution architecture, data models, and integration patterns, in a regulated industry.
Deep technical knowledge of data architecture, solution design, and cloud platforms, balanced with strategic capability.
Working proficiency with SQL and Python to evaluate and validate solution designs.
Strong business analysis and requirements-definition skills, including translating ambiguous business and scientific requirements into technical designs.
Working knowledge of cloud-based data platforms such as Snowflake, Databricks, AWS, Azure, or GCP data services, sufficient to design solutions for partner teams to build and operate.
Experience designing data models and integration patterns that connect enterprise systems such as ERP, CRM, LIMS/ELN, and clinical systems to analytics and AI platforms.
Familiarity with modern data-stack components, including warehouse infrastructure, streaming ingestion, semantic/metrics layers, governance and cataloging tools, and orchestration frameworks.
Experience defining data governance, MDM, or metadata-management requirements in a regulated or enterprise environment.
Practical experience designing data solutions for AI/ML or generative AI use cases, such as RAG pipelines, feature stores, embeddings, or vector databases.
Demonstrated success partnering with Data Science/Analytics leadership as a peer stakeholder.
Excellent cross-functional communication skills, including presenting solution vision and architecture to executive stakeholders.
Ability to operate effectively as a hands-on solution architect and designer in fast-paced, lean, resource-constrained organizations.
Strong product and solution mindset, treating data and solutions as products and services rather than only projects.
Clear, structured communication skills and ability to explain technical concepts to engineers and business or scientific stakeholders.
Entrepreneurial mindset, high tolerance for ambiguity, and bias for action.
Strong business acumen, including understanding how data and AI investments connect to pipeline value, competitive differentiation, and patient outcomes.
Commitment to responsible AI, data ethics, patient privacy, and regulatory compliance.

Nice To Haves

Advanced degree (M.S. or MBA).
5–7 years of experience in data engineering, solution architecture, or enterprise data/AI solution design.
Experience in life sciences, biotechnology, or pharmaceutical environments, including familiarity with clinical, regulatory, or scientific data types.
Familiarity with GxP data-integrity requirements, including 21 CFR Part 11 and ALCOA+, and public-company data controls such as SOX.
Experience with data cataloging and governance tools such as Collibra, Alation, Purview, or Unity Catalog.
Hands-on familiarity with generative AI platforms such as Azure OpenAI Service, AWS Bedrock, or Anthropic Claude API, including validated deployment in a regulated environment.
Hands-on understanding of how LLMs and AI agents consume data, including semantic layers, embeddings, vector search, and tool-use patterns such as MCP and function calling.
Track record designing data-as-a-product solutions, including APIs and self-service platforms for internal teams and functional partners.
Familiarity with enterprise architecture frameworks such as TOGAF and enterprise integration patterns.
Experience partnering with delivery teams using SDLC or Agile/Scrum methodologies to hand off solution designs for execution.

Responsibilities

Lead solution architecture and design for enterprise data and AI initiatives, translating business and scientific requirements into technical designs, data models, and integration patterns.
Define solution patterns, reference architectures, and reusable templates for the Solution Delivery and Data Platform Engineering teams.
Own requirements definition and business analysis for complex, cross-functional data and AI initiatives; partner with stakeholders to clarify and document needs.
Develop proof-of-concepts, prototypes, and solution blueprints to validate approaches and reduce design risk before handoff to Solution Delivery.
Define acceptance criteria and solution success measures for delivery teams.
Own and prioritize the enterprise data and AI solutions roadmap and intake process with business, scientific, and technology stakeholders.
Partner with Data Platform Engineering and Enterprise Architecture to define data, API, and platform requirements, including interfaces and access patterns for people and AI agents.
Define solution-level semantic-layer requirements, including governed metric definitions, lineage, and natural-language access patterns.
Advise on data warehouse/lakehouse, orchestration, streaming, and low-latency data-access requirements for solution use cases.
Evaluate emerging data and AI tools, including MCP servers, vector databases, and low-code/no-code platforms, with Data Platform Engineering.
Define enterprise data management requirements and standards for governance, master data management, metadata, cataloging, and lineage.
Define and enforce data standards, taxonomies, and ontologies that support data quality, consistency, and discoverability.
Partner with functional data owners and stewardship programs to ensure solution designs account for data quality and integrity.
Ensure solution designs address applicable regulatory and public-company requirements, including FDA 21 CFR Part 11, ALCOA+ principles, GDPR/HIPAA as applicable, and appropriate SOX controls.
Partner with Legal, Regulatory Affairs, Quality, and Internal Audit to incorporate data integrity, privacy, and access-control requirements into solution designs.
Define the vision and design for enterprise data products, including curated, documented, reusable datasets and APIs delivered as services.
Design the data-product intake model, service levels, catalog structure, and self-service access patterns for operationalization by Solution Delivery.
Translate business and scientific needs into data and AI solution designs, dashboards, and analytics-ready data specifications.
Define and use solution and data-product quality, adoption, and value metrics to improve designs.
Design solutions that enable AI agents to draft queries, detect anomalies, generate insights, and surface recommendations.
Define solution designs for AI/ML workloads, including model training data, feature serving, and RAG retrieval.
Apply knowledge of how LLMs and AI agents consume data, including semantic layers, embeddings, vector search, and tool-use patterns such as MCP and function calling.
Evaluate and recommend AI-assisted tools, such as automated data-quality checks and natural-language-to-SQL, for future solution designs.
Define data-foundation requirements for enterprise AI solutions, ensuring source data is clean, current, well-structured, and governed before use as AI context.
Architect knowledge bases, vector stores/embeddings, and semantic layers so enterprise data can be retrieved and used reliably by AI-enabled solutions.
Partner with AI and data science teams to define requirements for pipelines feeding AI/ML and generative AI solutions, including monitoring for data drift, freshness, and quality.
Define access-control, redaction/masking, and audit-logging requirements for data used in AI contexts.
Define documentation and validation requirements for AI data solutions in a regulated, GxP-adjacent environment.
Own and unify the data and AI solution-design roadmap across solution architecture, data management, data products, and AI enablement; serve as the primary point of accountability for solution design.
Champion a solution-design culture grounded in disciplined requirements, architecture rigor, and reusability; advise on vendor and tooling selection with procurement and delivery teams.
Define and report KPIs/OKRs for solution-design quality, requirements clarity, reuse of patterns, and stakeholder satisfaction.
Collaborate with IT Operations, Solution Delivery, Data Platform Engineering, Cybersecurity, and Enterprise Architecture to ensure designs are secure, integrated, and consistent with the broader IT landscape.