VP of Data Architecture

Conga•Houston, MA

1d•Remote

About The Position

Conga is seeking a VP of Data Architecture to define and lead the enterprise data strategy for a unified, governed, and scalable data ecosystem across all Conga platforms, including Salesforce-native products, the Advantage platform, and the integrated PROS pricing platform. This role operates as a key member of the technology leadership team, partnering with other senior leaders to establish data architecture, governance, and utilization strategies. The objective is to drive the long-term vision for data modeling, integration, and consumption to power product experiences, analytics, and AI innovation at scale. This is an end-to-end ownership role focused on ensuring a consistent, reliable, and future-ready data foundation for the business. The position addresses fragmentation across Conga's multiple product surfaces, aiming to enable consistent customer experiences, seamless integrations, and trusted, AI-ready data, thereby influencing product innovation, platform scalability, and the company's integrated SaaS operations.

Requirements

12+ years of experience in data architecture, data engineering, or related disciplines, including senior leadership roles.
Demonstrated success defining and scaling enterprise data strategies for SaaS platforms, including multi-tenant architectures.
Experience leading large-scale data transformations spanning legacy and modern cloud ecosystems.
Deep expertise in Salesforce data models and enterprise-scale integration patterns.
Proven ability to design systems supporting both real-time product experiences and analytics/AI workloads.
Strong knowledge of modern data technologies (e.g., Snowflake, Databricks, BigQuery, Redshift).
Experience with event streaming, CDC, ETL/ELT, and API-driven architectures.
Familiarity with governance, lineage, and cataloging tools (e.g., Collibra, Alation).
Working knowledge of AI/ML data infrastructure, including feature stores and vector-based systems.
Executive-Level Architectural Leadership: Ability to set direction and make high-impact decisions that shape the company’s data strategy for the long term.
Strategic Communicator: Ability to translate complex technical concepts into clear business outcomes for executive stakeholders.
Cross-Functional Influence: Ability to drive alignment across engineering, product, and business teams in highly matrixed environments.
Operational Excellence & Scale: Ability to build systems, practices, and teams that scale with the business while maintaining consistency and quality.

Nice To Haves

Experience in CLM, CPQ, or revenue operations platforms.
Familiarity with PROS pricing platform data architecture.
Exposure to advanced AI architectures and data platforms supporting agentic or generative AI.
Experience in private equity-backed or high-growth transformation environments.
Thought leadership through publications, speaking engagements, or open-source contributions.

Responsibilities

Define and own the enterprise data architecture vision, roadmap, and governance model across all platforms.
Establish canonical data models and a single source of truth strategy across product surfaces.
Drive architectural standards for how data is modeled, stored, integrated, and exposed.
Lead decisions on data platform strategy (warehouse, lakehouse, hybrid) aligned with long-term business needs.
Define scalable patterns for data ingestion, transformation, and synchronization across Salesforce, microservices, APIs, and third-party systems.
Establish enterprise-wide strategies for event-driven and batch data integration, including latency and performance standards.
Lead efforts to modernize legacy data architectures and reduce technical debt across the ecosystem.
Partner with Engineering and Product to design a unified, UI-facing data layer supporting product experiences and external integrations.
Define standards for APIs (REST/GraphQL), data contracts, and abstraction layers that enable decoupled, scalable development.
Drive strategies for performance optimization, including caching, pre-aggregation, and materialization.
Establish and enforce enterprise data governance frameworks aligned to SOC 2 and regulatory requirements.
Define standards for data quality, lineage, auditability, access controls, retention, and deletion.
Ensure consistent data stewardship, ownership, and accountability across domains.
Enable enterprise AI initiatives by defining reliable, high-quality, and well-governed data foundations.
Oversee architecture for feature stores, observability frameworks, and advanced data pipelines.
Partner with AI/ML teams to ensure scalable and production-ready data capabilities.
Build, lead, and mentor a high-performing data architecture and engineering function.
Influence senior stakeholders across Engineering, Product, and GTM to drive alignment on data strategy.
Establish architectural governance processes, including decision frameworks and ADRs, across the organization.
Act as a trusted advisor to executive leadership on data strategy, risk, and investment decisions.