This position leads the enterprise data architecture strategy and execution across CRM and customer data domains, data science (including LLM-enabled solutions), and modern data platforms. The SVP will partner with business and technology leaders to define the target-state architecture, establish governance and standards, and deliver scalable, secure, and high-quality data products. The role includes direct leadership of a team of 10–15 engineers/developers and accountability for building a culture of engineering excellence and continuous delivery.

Key Responsibilities

• Enterprise Data Architecture Leadership: Own and evolve the enterprise data architecture vision, reference architecture, and roadmap (current-state assessment, target-state design, transition plans).
• CRM & Customer Data: Design and govern scalable CRM data models and integration patterns; enable a unified customer view across channels and downstream consumers.
• Data Science & AI (LLM): Partner with data science teams to operationalize ML/LLM use cases; define patterns for feature/data access, prompt/response data management, evaluation, and model risk controls.
• Data Engineering & Integration: Guide design and implementation of robust ingestion and streaming pipelines using Python scripting and modern integration patterns (batch/near-real-time), including data loading and orchestration standards.
• Search & Event Streaming Platforms: Provide technical direction for Elasticsearch-based search/observability use cases and Kafka-based streaming/event-driven architectures.
• Analytics & BI Enablement: Establish trusted data layers, semantic models, and governed datasets to support analytics tools and business intelligence reporting.
• Governance, Quality & Security: Define and enforce standards for data quality, lineage, metadata, retention, privacy, and access controls in partnership with security, risk, and compliance.
• Technology Strategy & Stakeholder Management: Translate business strategy into technology outcomes; influence across executive stakeholders; communicate tradeoffs, investment needs, and delivery plans.
• People Leadership: Lead, coach, and develop a team of 10–15 developers/engineers; set clear goals, foster accountability, and build a high-performing, inclusive culture.
• Delivery Excellence: Drive agile execution, engineering best practices (CI/CD, testing, observability), and operational readiness; ensure predictable delivery with measurable outcomes.

Required Qualifications

• 15+ years of progressive experience in technology, data architecture (SQL), and data engineering, including leadership at the enterprise/platform level.
• Proven experience defining and implementing enterprise data architecture, including conceptual/logical/physical modeling and integration patterns.
• Hands-on understanding of CRM data domains and architectures (e.g., customer 360, master/reference data, identity/resolution, consent/preferences).
• Strong background in data science enablement, including productionizing ML and LLM-related solutions (data pipelines, evaluation, governance, monitoring).
• Proficiency with Python for data ingestion/loading, automation, and scripting in data engineering contexts.
• Experience with Kafka (or equivalent event streaming) and event-driven integration patterns.
• Experience with Elasticsearch (or equivalent search/analytics engine) for search, indexing, and high-volume query use cases.
• Experience enabling analytics and BI reporting ecosystems, including semantic layers, metric definitions, and governed self-service data access.
• Demonstrated people-management experience leading teams of 10+ engineers/developers, including hiring, performance management, and talent development.
• Strong executive communication skills with an ability to influence cross-functional leaders and drive alignment on architecture decisions.

Preferred Qualifications

• Experience modernizing legacy data ecosystems toward cloud-based or hybrid architectures and operating models.
• Familiarity with data governance frameworks and tooling for catalog/metadata, lineage, and data quality management.
• Experience with MLOps/LLMOps practices (model/prompt versioning, experimentation tracking, automated evaluation, observability).
• Experience in regulated environments and implementing privacy-by-design, auditability, and information security controls.
• Exposure to enterprise integration patterns (API-first, CDC, streaming, ETL/ELT) and orchestration/automation practices.

Leadership Competencies

• Strategic mindset: Balances long