Data Strategy Lead, Digital Client Experience

Jobtailor; Massachusetts; On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor is seeking a senior leader to define and execute a DCX data and knowledge strategy, turning proprietary information into reusable data products. You will prioritize high-value domains with cross-functional partners, building scalable knowledge assets across risk, client intelligence, and policy wording.

You will embed analytics into everyday decision-making, define semantic standards, and establish governance for AI-ready knowledge products.

Qualifications

• Proven experience in data strategy, data product management, enterprise data platforms, knowledge management, AI enablement, or digital product leadership.
• Bridge analytics and operations, translating data insight into operational practice and vice versa.
• Strong understanding of retrieval-augmented generation, vector search, metadata, semantic layers, knowledge graphs, and agentic workflows.
• Experience with complex enterprise data environments, fragmented systems, inconsistent data quality, and mixed structured and unstructured sources.
• Ability to partner with senior business leaders, technology teams, data governance, legal, compliance, and product teams.
• Experience defining data ownership, stewardship models, business glossaries, data quality frameworks, or domain data products.
• Strong product mindset connecting technical enablement to business value and user adoption.
• Ability to operate in ambiguity and create structure across complex, cross-functional environments.
• Experience in financial services, insurance, risk advisory, professional services, or another data-rich regulated industry.
• Familiarity with modern lakehouse, data catalogue, data governance, and AI platform architectures.
• Familiarity with Databricks or comparable unified data and AI platforms, including Spark-based lakehouse environments.
• Experience preparing proprietary document corpora for AI search, summarization, and reasoning.
• Exposure to ontology design, knowledge graphs, semantic modeling, or entity resolution.
• Working knowledge of enterprise AI governance, model evaluation, responsible AI, or AI risk controls.
• Hands-on experience building or scaling data products for client-facing or colleague-facing digital platforms.
• Experience enabling teams to adopt new data, knowledge, or AI capabilities in sustainable ways.

Responsibilities

• Define the DCX data and knowledge strategy and roadmap for converting proprietary information into reusable knowledge and data products.
• Prioritize high-value domains and use cases with business, content, product, and operations partners.
• Build reusable knowledge products across client intelligence, claims intelligence, exposure data, risk engineering insight, benchmarking, policy wording, market appetite, and industry risk profiles.
• Embed analytics and insight into workflows, processes, and day-to-day decision-making.
• Define the knowledge layer, including business entities, relationships, taxonomies, ontologies, semantic standards, and entity resolution.
• Establish governance, trust, evaluation standards, and operating models for AI-ready knowledge products.
• Support AI and automation for discovery, classification, metadata extraction, data profiling, duplicate detection, and corpus preparation.
• Create feedback loops to improve metadata, retrieval, quality, and usability of knowledge assets.
• Define AI-readiness requirements for reports, presentations, contracts, broker notes, risk assessments, policy wording, claims summaries, and client deliverables.
• Partner with engineering teams to shape tools that search, retrieve, query, summarize, compare, and evaluate knowledge assets.
• Contribute to priority DCX workstreams and translate ideas into practical solutions.

Skills

Data Strategy

Knowledge Management

AI Enablement

Data Governance

Product Management

Tools

Databricks

Spark Lakehouse

Data Catalogue

AI Platforms

Enterprise Data Platforms

Job description

• Define the DCX data and knowledge strategy and roadmap for converting proprietary information into reusable knowledge and data products


• Prioritize high-value domains and use cases with business, content, product, and operations partners


• Build reusable knowledge products across client intelligence, claims intelligence, exposure data, risk engineering insight, benchmarking, policy wording, market appetite, and industry risk profiles


• Embed analytics and insight into workflows, processes, and day-to-day decision-making


• Define the knowledge layer, including business entities, relationships, taxonomies, ontologies, semantic standards, and entity resolution


• Establish governance, trust, evaluation standards, and operating models for AI-ready knowledge products


• Support AI and automation for discovery, classification, metadata extraction, data profiling, duplicate detection, and corpus preparation


• Create feedback loops to improve metadata, retrieval, quality, and usability of knowledge assets


• Define AI-readiness requirements for reports, presentations, contracts, broker notes, risk assessments, policy wording, claims summaries, and client deliverables


• Partner with engineering teams to shape tools that search, retrieve, query, summarize, compare, and evaluate knowledge assets


• Contribute to priority DCX workstreams and translate ideas into practical solutions

Requirements

• Proven experience in data strategy, data product management, enterprise data platforms, knowledge management, AI enablement, or digital product leadership


• Ability to bridge analytics and operations, translating data insight into operational practice and vice versa


• Strong understanding of retrieval-augmented generation, vector search, metadata, semantic layers, knowledge graphs, and agentic workflows


• Experience with complex enterprise data environments, fragmented systems, inconsistent data quality, and mixed structured and unstructured sources


• Ability to partner with senior business leaders, technology teams, data governance, legal, compliance, and product teams


• Experience defining data ownership, stewardship models, business glossaries, data quality frameworks, or domain data products


• Strong product mindset connecting technical enablement to business value and user adoption


• Ability to operate in ambiguity and create structure across complex, cross-functional environments


• Experience in financial services, insurance, risk advisory, professional services, or another data-rich regulated industry


• Familiarity with modern lakehouse, data catalogue, data governance, and AI platform architectures


• Familiarity with Databricks or comparable unified data and AI platforms, including Spark-based lakehouse environments


• Experience preparing proprietary document corpora for AI search, summarization, and reasoning


• Exposure to ontology design, knowledge graphs, semantic modeling, or entity resolution


• Working knowledge of enterprise AI governance, model evaluation, responsible AI, or AI risk controls


• Hands‑on experience building or scaling data products for client‑facing or colleague‑facing digital platforms


• Experience enabling teams to adopt new data, knowledge, or AI capabilities in sustainable ways

Core Competencies

Demonstrates expertise in data strategy, knowledge management, and AI enablement, with a strong focus on building reusable knowledge products and embedding analytics into operational workflows. Proven ability to partner with cross-functional teams to drive data quality and governance in complex enterprise environments.

Highest-signal resume keywords

• Data Strategy


• Knowledge Management


• AI Enablement


• Data Governance


• Product Management

Hard Skills

• Data Product Management


• Retrieval-Augmented Generation


• Metadata Management


• Knowledge Graphs


• Entity Resolution


• Data Quality Frameworks


• Ontology Design


• AI Risk Controls


• Data Profiling


• Document Corpora Preparation

Soft Skills

• Operational Practice Translation


• Cross-Functional Collaboration


• Adaptability in Ambiguity


• User Adoption Focus


• Stakeholder Partnership

Industry Keywords

• Financial Services


• Insurance


• Risk Advisory


• Professional Services


• Data-Rich Regulated Industry

Tools & Technologies

• Databricks


• Spark-Based Lakehouse


• Data Catalogue


• AI Platforms


• Enterprise Data Platforms