[Hiring] IT Enterprise Data Architect @Convene

Role Description

The IT Enterprise Data Architect defines, owns, and drives the company enterprise data model across the internal/Corporate technology estate. This is a senior individual-contributor role centered on the purposeful, well-reasoned design of data repositories, canonical objects, classification schemes, and field-level mappings — including data as it moves through the Boomi integration layer.

• The architect establishes the semantic and canonical data layers and the reusable data constructs that underpin the company's enterprise AI development, and sets the standards that data engineering, business applications, and integration teams build against.
• This is a hands-on architect — not solely a "drawer" of models: the individual is accountable for the blueprint and standards (the "what" and "why" of enterprise data) and is equally comfortable working directly in the data tools to profile, query, validate, and review data.
• They set standards while working in close partnership with the teams that build and operate the pipelines and platforms.

Qualifications

• Deep, hands-on experience architecting data in a highly customized Salesforce environment — custom objects, schema, and data model. (Must-have.)
• Demonstrated, hands-on experience with Snowflake and Boomi. (Must-have.)
• Deep, hands-on proficiency with the data tools used day-to-day for analysis and review — SQL, Salesforce (SFDC), Microsoft Fabric, and Snowflake. (Must-have.)
• Experience designing and enforcing a system-of-record model across a multi-system estate — defining canonical keys, cross-system identity spine design, and SoR governance. (Must-have.)
• Proven track record defining enterprise data models, canonical and semantic layers, and classification/taxonomy standards.
• Strong command of integration architecture and field-level data mapping across enterprise systems.
• Experience designing data constructs that support AI/ML and analytics use cases.
• Solid data governance, metadata, lineage, and data-quality fundamentals.
• Experience driving large-scale, evidence-based data quality remediation — measured profiling, root-cause analysis, remediation sequencing, and tracking outcomes to closure across a complex estate.
• Experience operating in or facilitating an architecture governance body — running design reviews, managing an open decisions register, writing decision records, and driving cross-functional alignment without direct authority.
• Proven experience converging a post-M&A or post-growth application estate onto a rationalized, canonical model — not greenfield architecture, but remediation and convergence in a complex, heavily-customized environment.
• Ability to set standards and influence across teams without direct authority; strong communication with both technical and business stakeholders.
• 10+ years in data architecture, data modeling, or enterprise data roles.

Requirements

• Hands-on experience with NetSuite (ERP) data architecture.
• Experience with Zone Advanced Billing (ZAB) or comparable NetSuite-native subscription billing engines — ZAB subscription model, usage charge objects, and billing pipeline data design.
• Experience enabling enterprise AI/GenAI initiatives (semantic layers, retrieval-ready/RAG data, feature stores).
• Experience with usage-based or consumption-based billing data models — including usage staging, aggregation pipelines, and multi-dimensional product calculation logic.
• Experience with identity resolution at scale — designing or operating matching algorithms, golden-record patterns, or SSO-to-CRM linking models across hundreds of thousands of accounts.
• Experience mapping and integrating data through mergers & acquisitions (M&A) or system-consolidation programs.
• Familiarity with data governance frameworks (e.g., DAMA-DMBOK) and data catalog/metadata tooling.
• Experience in a SaaS or technology-company environment.
• Relevant certifications (Salesforce, Snowflake, Boomi).
• Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent experience.

Benefits

• Competitive benefits.
• High-growth opportunities.

First 6–12 Months — Indicators of Success

• Baseline the current-state enterprise data landscape, key repositories, and existing Boomi mappings.
• Deliver a v1 enterprise data model with documented canonical objects and classification standards.
• Establish field-level mapping standards adopted through the Boomi integration layer.
• Define the semantic-layer approach that enables at least one prioritized enterprise AI use case.
• Stand up the Data Architecture Working Group governance cadence — recurring review, decision register process, and intake gate — and close the first batch of open design decisions.
• Define the account de-duplication and identity convergence approach.
• Establish measurable DQ baselines for the top 5 High-severity findings.