IT Ent. Data Architect

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. The posting includes: About Convene Inc. (company description) maybe not needed as heading? Could be "About the role"? Actually "Position Summary" is description. We can treat that as "About the role". There's "Key Responsibilities". There's "Required Qualifications". There's "Preferred Qualifications". There's "Core Competencies". There's "First 6-12 Months — Indicators of Success". There's also "Reporting & Organizational Context". There's "Scope & Boundaries". There's "Mergers & Acquisitions (M&A) Data Integration" etc but those are within responsibilities. We need to decide which sections to include. Must not repeat job title. Must not include metadata like Job ID. Must not include boilerplate like EEO etc (none present). Must preserve all substantive info. We can group content logically: - About the role: could combine Position Summary and Reporting & Organizational Context? But we need headings only when there is real specific content. We could have sections: About the role (Position Summary), Responsibilities (Key Responsibilities), Qualifications (Required Qualifications), Preferred Qualifications (maybe as separate or part of Qualifications?), Skills (Core Competencies), Benefits? Not mentioned. Pay? Not mentioned. Schedule? Not mentioned. So we omit those. We also have "First 6-12 Months — Indicators of Success" could be a section like "Initial Goals" but not in list of allowed headings. Allowed headings are About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. "Requirements" maybe similar to Qualifications. We could map Required Qualifications to Requirements. Preferred Qualifications could be under Qualifications as well or maybe Skills? But better to keep Qualifications (covers both required and preferred). Could also have Skills for Core Competencies. Thus sections:

About the role

with Position Summary + Reporting & Organizational Context maybe.

Responsibilities

with Key Responsibilities (including subpoints).

Requirements

with Required Qualifications.

Skills

with Core Competencies. Optionally include

Preferred Qualifications

but not in allowed list; we could embed under Qualifications or Requirements? The instruction: use headings for those specific names; we should not create other headings. So we must not add Preferred Qualifications heading. So we need to incorporate preferred qualifications somewhere else, maybe within Qualifications or Skills. Since we have Requirements heading for required qualifications, we could add a paragraph after that for preferred qualifications, but that would be under Requirements heading? Might be okay to include both required and preferred under Requirements heading. Or we could have Qualifications heading for both required and preferred, and Skills for core competencies. Let's decide. Allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Thus we can have: - About the role (position summary + reporting context) - Responsibilities (key responsibilities) - Requirements (required qualifications) - Qualifications (preferred qualifications) - Skills (core competencies) - Maybe also include "First 6-12 Months — Indicators of Success" as a section? Not allowed heading; we could incorporate under Responsibilities or maybe under About the role? But it's specific content; we need to preserve it. Could we put it under a heading like "Responsibilities"? It's not responsibility but early goals. Could we create a section "Goals"? Not allowed. Perhaps we can put it under "Responsibilities" as a bullet list? Might be acceptable as it's still part of role expectations. However the instruction: "Prefer a few substantive sections over many thin ones." So we could add a section "First 6-12 Months — Indicators of Success" as a separate heading only if we map to an allowed heading? Not allowed. So we need to embed it somewhere else, maybe under Responsibilities as a subsection? But we cannot add headings inside? We could just add a

after Responsibilities heading with that content. That would preserve info. Similarly "Scope & Boundaries" could be embedded under Responsibilities or About the role. Thus final HTML:

About the role

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.

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.

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.

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; 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. (Details omitted as table not provided; but we keep description: owns enterprise data model, canonical & semantic layers, classification, field-level mapping standards; owns Boomi integration data design & mapping standards; owns M&A data mapping & integration; owns data architecture for Salesforce, Snowflake, and NetSuite corporate data domains; primary contributor/facilitator for Architecture Working Group; liaises with P&E; partners closely with Data Engineering and Information Security.)
• 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.

Requirements

• 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