About Convene Inc.
Convene, Inc. is a Tampa based, award-winning technology services organization with offices and resources throughout the US, Mexico, and India. We have successful, referenceable customers, competitive benefits, and high-growth opportunities.
Position Summary
● The IT Enterprise Data Architect defines, owns, and drives 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
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.
Reporting & Organizational Context
Reports to Enterprise Architect
Sits within: Internal IT
● Liaises with Product & Engineering (P&E): to ensure data alignment and interoperability — but is not
responsible or accountable for the P&E / product data domain.
● Partners closely with Data Engineering and Information Security: both of which sit outside of IT, on pipeline /
platform design and on data protection, classification, and compliance.
● Operates within and helps chair the Data Architecture Working Group: the cross-functional governance body
(IT, P&E, Business) that owns the canonical model, ratifies design decisions, and sets the architecture standards
the estate builds against.
Key Responsibilities
Enterprise Data Model & Architecture
● Own, define, and continuously evolve the enterprise data model spanning core corporate systems.
● Design data repositories, canonical objects, entity relationships, and the classification / taxonomy standards
that govern them — with clear, documented rationale for each decision.
● Establish and govern field-level mapping standards across systems and through the integration layer.
● Define data domains, ownership, lineage, and reference / master-data approaches; drive consistency and reuse
across the estate.
Semantic & Canonical Layers for Enterprise AI
● Define and maintain the semantic and canonical data layers that provide trustworthy, reusable data constructs
for enterprise AI development and analytics.
● Design data structures that are "AI-ready " — supporting retrieval, grounding, feature engineering, and
governed AI / ML use cases.
● Partner with Data Engineering and AI / automation teams to translate business concepts into consistent, well-defined
semantic models.
Integration Architecture (Boomi)
● Architect the data flows and canonical mappings implemented through the Boomi integration layer; define
integration patterns, data contracts, and reusable mapping standards.
● Ensure integrations preserve data integrity, classification, and lineage end-to-end.
● Provide design authority and hands-on guidance for how enterprise data is modeled and mapped within Boomi.
Mergers & Acquisitions (M&A) Data Integration
● Lead data mapping and integration for M&A activity — profiling acquired-company data, mapping it to
company''s canonical model and integrating it into the enterprise data estate.
● Build repeatable playbooks, mapping templates, and integration patterns (e.g., through Boomi) that accelerate
onboarding of acquired systems and data.
● Partner with Corporate Development, Data Engineering, and Information Security so acquired data is classified,
protected, and reconciled throughout integration.
Platform Data Architecture
● Provide data architecture leadership for a highly customized Salesforce environment — custom objects,
schema, and data model.
● Design and govern data structures within Snowflake as the enterprise data platform.
● Extend enterprise data architecture into NetSuite (ERP) data domains.
Hands-On Data Analysis & Review
● Work hands-on in the data — profiling, querying, and analyzing data directly to inform and validate design
decisions. This is a practitioner role, not solely a modeling or diagramming function.
● Use SQL and modern data tooling day-to-day (e.g., Snowflake, Microsoft Fabric, Salesforce) to investigate data
quality, validate field-level mappings, and review integrations.
● Review and troubleshoot data issues across systems and the integration layer, partnering with Data Engineering
on root-cause analysis and remediation.
Data Standards, Classification & Governance
● Establish enterprise data standards, naming conventions, and data classification policies.
● Partner with Information Security on data classification, protection, and compliance requirements.
● Champion data quality, metadata, and lineage practices that make enterprise data trustworthy and reusable.
Architecture Working Group & Decision Governance
● Operate as a primary voice in the Data Architecture Working Group (WG) — the cross-functional governance
body that owns the canonical model and standards, ratifies systems of record, and gates initiative intake.
● Maintain and drive closure of the Open Decisions Register
● Support maintenance of the Enterprise Architecture Master as a living, versioned document
Cross-Functional Collaboration
● Liaise with P&E on interoperability and data alignment, without owning the product data domain.
● Partner closely with Data Engineering (outside IT) on pipeline and platform design and delivery.
● Collaborate with Business Applications, Integration, and Analytics teams to turn the model into implementation.
Scope & Boundaries
This role is deliberately scoped. The table below clarifies where the architect is accountable versus where they align with
or support other teams.
Function / Domain This Role''s Relationship
Enterprise data model, canonical & semantic layers, classification, field-level mapping standards
Owns & drives
Boomi integration data design & mapping standards Owns (design & standards)
M&A data mapping & integration Owns & drives Salesforce, Snowflake, and NetSuite corporate data domains
Owns data architecture
Architecture Working Group — open decisions & governance cadence
Primary contributor / facilitator
Product & Engineering (P&E) data domain Liaises / aligns — not accountable
Data Engineering team (outside IT) Partners closely
Information Security team (outside IT) Partners closely
Required 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.
Preferred Qualifications
● 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.
Core Competencies
● Systems thinking and structured, first-principles design.
● Translating ambiguous business needs into durable, well-reasoned data models.
● Influence without authority and disciplined standards stewardship.
● Clear technical writing and documentation of design decisions and rationale.
● Hands-on practitioner mindset — equally comfortable designing the model and working directly in the data to
prove it out.
● Convergence mindset — comfortable inheriting a complex, imperfect estate and making it progressively better
through sequenced, evidence-based decisions rather than seeking to rebuild from scratch.
● Governing in ambiguity — the ability to maintain a clear, ratified architecture direction while adjacent programs
(P&P, Zone, PI) are actively building and creating new constraints.
● Measured remediation discipline — the ability to size, prioritize, and sequence a data quality backlog by
business impact, not just technical severity.
First 6–12 Months — Indicators of Success
1. Baseline the current-state enterprise data landscape, key repositories, and existing Boomi mappings.
2. Deliver a v1 enterprise data model with documented canonical objects and classification standards.
3. Establish field-level mapping standards adopted through the Boomi integration layer.
4. Define the semantic-layer approach that enables at least one prioritized enterprise AI use case.
5. 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
6. Define the account de-duplication and identity convergence approach
7. Establish measurable DQ baselines for the top 5 High-severity findings
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