Vice President Of Data Processing Jobs (NOW HIRING)

VP, Data Architecture
Location: Remote (US) with periodic travel to engineering hubs
Reports to: Chief Technology Officer

A quick snapshot...

As the Vice President of Data Architecture at Conga, you will define and lead the enterprise data strategy that underpins a unified, governed, and scalable data ecosystem across all Conga platforms. This spans the Salesforce-native product portfolio, the Advantage platform (cloud-native SaaS), and the integrated PROS pricing platform.

Operating as a key member of the technology leadership team, you will partner closely with the VP of Platform Engineering and other senior leaders to establish how data is architected, governed, and leveraged across the organization. You will drive the long-term vision for how data is modeled, integrated, and consumed to power product experiences, analytics, and AI innovation at scale.

This is not an execution-only role-this is end-to-end ownership of Conga's enterprise data architecture strategy, ensuring the business has a consistent, reliable, and future-ready data foundation.

Why it's a big deal...

Conga operates across multiple product surfaces with divergent data models and architectures, creating fragmentation that impacts customer experience, data reliability, and the effectiveness of AI-driven capabilities.

As VP of Data Architecture, you will lead the transformation toward a unified and scalable data ecosystem, enabling consistent customer experiences, seamless integrations, and trusted, AI-ready data.

Your leadership will directly influence product innovation, platform scalability, and Conga's ability to operate as a truly integrated SaaS business.

What you'll lead...

Enterprise Data Strategy & Architecture

• 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.

Data Integration & Platform Modernization

• 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.

Data Consumption & Product Enablement

• 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.

Governance, Trust & Compliance

• 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.

AI & Analytics Enablement

• 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.

Leadership & Organizational Impact

• 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.

Are you the person we're looking for?

A proven track record...

• 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.

Key Competencies

Executive-Level Architectural Leadership
You set direction and make high-impact decisions that shape the company's data strategy for the long term.

Strategic Communicator
You translate complex technical concepts into clear business outcomes for executive stakeholders.

Cross-Functional Influence
You drive alignment across engineering, product, and business teams in highly matrixed environments.

Operational Excellence & Scale
You build systems, practices, and teams that scale with the business while maintaining consistency and quality.

Here's what will give you an edge...

• 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.

What we value...

• Curiosity & Continuous Learning: Evolving with the data and AI landscape.
• Radical Ownership: Treating data as a core business asset.
• Transparency: Open communication and proactive risk management.
• Bias for Action: Balancing speed with architectural rigor.
• Engineering Excellence: Building scalable, resilient, and future-ready systems.