Key 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.
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.
Preferred Qualifications
- 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.
Key Competencies
- 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
Deliverables (Expected Outputs)
- 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).
