Data Architect / QA Lead (SS21) POST NUMBER: 482078 [Remote]

Data Architect / QA Lead
DETAILS
Location : Remote
Position Type : 16M Contract
Hourly / Salary : to $90W2 (based on experience)

JOB SUMMARY
Vaco is currently seeking a Data Architect / QA Lead for a 16M Contract opportunity that is remote. The Data Architect / QA Lead will support a large-scale enterprise data initiative focused on designing high-quality analytical data models while ensuring consistency, accuracy, and governance across reporting assets. The Data Architect / QA Lead will serve as the bridge between architecture, business stakeholders, and engineering teams by defining semantic models, validating KPI calculations, reviewing data quality, and establishing rigorous quality assurance standards throughout the data lifecycle.  The ideal Data Architect / QA Lead will have a combination of strong enterprise data architecture experience with a passion for data quality and governance. This is not a traditional ETL development role, the focus is on designing the data foundation, validating business logic, and ensuring analytical outputs accurately reflect business requirements.

• Enterprise Data Architecture – Designing Enterprise Data Architectures Supporting Scalable Reporting / Analytics / Enterprise Data Initiatives
• Enterprise Data Modeling – Developing Domain-Driven Data Models Aligned with Business Capabilities / Enterprise Data Domains / Organizational Data Strategy
• Semantic Modeling / Business Definitions – Creating / Maintaining Semantic Models that Standardize Business Terminology / KPI Definitions / Reporting Logic Across Enterprise Reporting Platforms
• Enterprise KPI Governance – Partnering with Business Stakeholders to Define / Validate / Standardize Enterprise KPIs / Business Metrics / Calculation Logic Across the Organization
• Data Validation / Reconciliation – Performing Source-to-Target Data Validation / Reconciliation / Completeness Verification to Ensure Accurate / Trusted / Consistent Reporting
• Architecture / Data Model Governance – Conducting Architecture Reviews / Data Model Reviews / Design Validation to Ensure Alignment with Enterprise Standards / Scalability / Best Practices
• Data Quality / QA Governance – Leading Data Quality Governance / QA Reviews / Validation Processes Throughout the Enterprise Data Lifecycle
• Delivery Governance / Definition of Done – Establishing Definition of Done Criteria for Data Engineering Deliverables Including Architecture / Testing / Reconciliation / Documentation / Business Validation
• Enterprise Data Modeling / Performance – Reviewing Dimensional Models / Semantic Layers / Data Structures to Improve Scalability / Reporting Performance / End-User Usability
• Cross-Functional Collaboration – Partnering with Data Engineers / BI Developers / QA Teams / Business SMEs Throughout Solution Design / Development / Testing / Production Delivery
• Data Quality Improvement – Identifying Data Quality Issues / Inconsistencies / Data Gaps | Driving Root Cause Analysis / Remediation / Continuous Improvement Initiatives
• Metadata / Business Glossary Governance – Standardizing Business Terminology / Metadata / Semantic Definitions / Data Domain Consistency Across the Enterprise
• Enterprise Data Governance – Supporting Enterprise Governance Initiatives Related to Data Modeling / Data Quality / Metadata Management / Data Lineage / Reporting Consistency

JOB REQUIREMENTS

• Snowflake – Designing / Validating Enterprise Data Warehouse Solutions within Snowflake Supporting Scalable Reporting / Analytics / Governed Data Consumption
• Enterprise Data Architecture – Designing Logical / Conceptual Data Architectures Aligned with Enterprise Reporting / Analytics / Business Objectives
• Domain-Driven Design (DDD) – Developing Domain-Driven Data Models Organized Around Business Capabilities / Enterprise Data Domains / Long-Term Scalability
• Semantic Modeling / Business Definitions – Creating / Maintaining Semantic Models that Standardize Business Definitions / Reusable Metrics / Reporting Logic Across BI / Analytics Platforms
• Enterprise Data Modeling – Designing Logical / Conceptual / Dimensional Data Models Supporting Scalable / High-Performance Analytical Solutions
• Enterprise KPI Governance – Partnering with Business Stakeholders to Define / Document / Validate / Standardize Enterprise KPIs / Business Metrics / Calculation Logic
• Data Validation / Reconciliation – Performing Source-to-Target Data Validation / Reconciliation / Completeness Verification to Ensure Accurate / Trusted / Consistent Data
• Data Quality / QA Governance – Establishing / Executing Data Quality Validation Processes / QA Reviews / Governance Checkpoints Prior to Production Deployment
• Delivery Governance / Definition of Done – Defining / Enforcing Definition of Done Criteria for Data Engineering Deliverables Including Architecture / Testing / Reconciliation / Documentation / Business Validation
• Architecture / Design Reviews – Conducting Architecture Reviews / Data Model Reviews / Semantic Layer Reviews to Ensure Alignment with Enterprise Standards / Scalability / Performance / Best Practices
• Cross-Functional Collaboration – Partnering with Business Stakeholders / Data Engineers / BI Developers / QA Teams / Data Governance Teams Throughout Solution Design / Development / Delivery
• Analytical Problem Solving – Identifying Data Quality Issues / Reporting Discrepancies / Business Rule Gaps / Data Inconsistencies | Driving Root Cause Analysis / Remediation / Continuous Improvement Initiatives

PREFERRED (not required)

• Data Vault – Designing / Supporting Data Vault Architectures for Scalable Enterprise Data Warehouse Solutions
• Kimball Methodology – Designing Dimensional Models (Star Schemas / Fact Tables / Conformed Dimensions) | Supporting Enterprise Reporting / Business Intelligence
• Enterprise Data Governance – Supporting Enterprise Data Governance / Metadata Management / Business Glossary Initiatives
• BI Platforms – Experience Supporting Modern BI / Reporting Platforms Including Power BI / Tableau / Looker
• Agile Data Delivery –Working in Agile Data Engineering / Analytics Delivery Teams Supporting Iterative Solution Development / Delivery

Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual’s skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company’s 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products.