Description

Responsibilities:

• Process & Technology Review - Assess end-to-end data modeling lifecycle processes (intake, design, review/approval, governance, versioning, publication, change management, and release).
• Evaluate tooling and integrations (data modeling tools, metadata/catalog, version control, CI/CD, ticketing/work management).
• Identify gaps, risks, bottlenecks, and control weaknesses; document findings and prioritized recommendations.
• Review alignment to enterprise standards (naming conventions, modeling patterns, domain boundaries, stewardship, metadata/lineage expectations).
• Manual Testing Create and execute manual test plans and test cases for key workflows, including:
• Model creation/updates (conceptual/logical/physical as applicable)
• Standards validation (naming, datatypes, keys, relationships, referential integrity)
• Model to -DDL generation and deployment readiness checks - Versioning/branching/merging and promotion processes - Metadata publishing and verification (catalog/glossary/lineage where applicable)
• Security and role-based access controls within tools
• Document test evidence, defects, and remediation recommendations; support triage and retesting.
• Test Strategy & Automation Roadmap
• Define a fit-for-purpose testing strategy for data modeling transformation outcomes (quality, governance, velocity, auditability).
• Identify automation candidates and define what should be automated vs. remain manual.
• Recommend an automation approach and integration points, potentially including:
• Automated standards checks (rule-based validation / linting)
• Model diffing and regression checks across versions
• CI/CD quality gates for model changes (PR checks, approvals, artifact packaging)
• Automated verification of model-to-implementation consistency (where feasible)
• Automated metadata publishing completeness checks
• Deliver a phased roadmap with dependencies, effort estimates, and measurable success criteria; optionally deliver a proof of concept if in scope.
• The contractor will perform an independent review of the current  process and technology ecosystem supporting data modeling, execute structured manual testing of critical workflows, and develop a practical, phased plan to automate testing and  quality gates.
• This role will partner with data modelers, data engineers, platform teams, and data governance stakeholders to improve quality, consistency, and release readiness
• of data modeling artifacts and related metadata.
• Can understand business requirements
• Write and execute test cases manually or using automation
• Analyze results of tests, defects tracking and management, report status and recommendations for modifications to test plan and/or schedule.

Requirements:

• Must have experience setting up testing and automation.
• Heavy experience with data modeling and a strong understanding of logical data  modeling.
• Advanced Excel skills using complex worksheets.
• Hands on development  with Java or Python, automation testing, AI/ML fluency, and SQL.
• Industry knowledge and data used in the mortgage industry.
• Client is seeking an experienced contractor to support an enterprise data modeling transformation initiative.
• Familiar with agile methodology
• Experience with: All phases of testing-system testing, SIT and UAT -Hewlett Packard’s ALM (Quality Center) version 11.0 testing tools -Microsoft Visio -Microsoft Office (Word, Excel, PowerPoint) -SQL
• Required Qualifications:
• 7+ years of experience in data engineering, data architecture, data modeling, QA, or related roles with a strong testing focus.
• Demonstrated experience testing data/metadata/modeling workflows (beyond  application UI testing).
• Strong knowledge of data modeling concepts: entities/relationships, keys, normalization, dimensional vs. relational patterns, naming/standards.
• Proven ability to develop test plans, write test cases, and execute structured manual testing with clear documentation.
• Strong analytical and communication skills; able to produce actionable assessment and roadmap deliverables.
• Experience with enterprise data modeling tools (e.g., ER/Studio, ERwin, SAP PowerDesigner, Sparx EA, or similar).
• Familiarity with metadata/catalog/governance platforms (e.g., Collibra, Alation, Informatica, Microsoft Purview).
• Experience with CI/CD and automation tooling (e.g., GitHub/GitLab, Azure DevOps, Jenkins) and scripting (Python preferred).
• Experience implementing automated quality checks (rules engines, schema validation, model diffing).
• Familiarity with common enterprise data platforms (e.g., Snowflake, Databricks, SQL Server, Oracle, PostgreSQL) and DDL deployment patterns.
• Process analysis and continuous improvement
• Manual testing discipline and defect management
• Test strategy development and automation planning
• Data governance and standards enforcement
• Stakeholder management across architecture, engineering, governance, and delivery teams
• Current-state process and technology assessment with prioritized recommendations.
• Manual test plan, test cases, execution results, and defect log.
• Future-state testing strategy and test automation roadmap (phased).
• Recommended KPIs/controls (e.g., standards compliance rate, defect leakage, cycle time, automation coverage).
• Optional: proof-of-concept automation scripts/pipeline examples (if agreed in scope).
• 8+ years of Automation Testing experience.
• Data Modeler / Data Modeling experience is required.
• Experience testing data, metadata, and data modeling workflows.
• Hands-on automation framework development using Java and/or Python.
• Experience with Cucumber framework.
• Strong SQL and JSON validation skills.
• Experience testing applications used for building and managing data models.
• Database experience with Snowflake and PostgreSQL.
• Data Modeling tools exposure (e.g., ER Studio, Erwin, Visio, etc.).
• Mortgage domain experience.
• AI/ML exposure

Key Skills

Education

Any Graduate