Global Practioner

About the role

This position calls for an experienced leader who architects durable data quality programs. The primary focus is guiding executive ownership and ensuring trusted outcomes. You will convert strategic consulting into reliable operations for both prospects and customers. Your contributions will ensure data integrity drives business value at scale. The role is situated within the Global Practitioner team and emphasizes strategic advisory and execution.

Key facts

What you'll do

You will design data quality operating models that clarify ownership and articulate the business rationale for stakeholders. Building rules and monitoring frameworks forms a core part of the work, ensuring issues are detected early and supporting observability within pipelines. You will establish remediation workflows that link findings to root-cause analysis and track measurable progress over time.

Guarding reliability is a key duty, achieved by embedding quality checks directly within data products and governance processes. You will facilitate and lead high-impact customer workshops. These sessions cover data quality strategy, use case prioritization, rule and metric design, roles and stewardship, and adoption tactics.

Executing maturity assessments allows you to benchmark current customer capabilities and shape phased roadmaps for improvement. Curating and sharing current best practices and compelling success stories is essential to demonstrate value across the Collibra ecosystem. You will partner closely with technical teams to define metrics, logic, and integrations by leveraging existing function libraries.

Invoking REST endpoints and applying boolean logic connects data quality findings to clear ownership. Reading patterns, outliers, and valid ranges allows for the refinement of rules and improved detection accuracy. This role requires a consultative approach to engage stakeholders and establish immediate credibility.

Requirements

You must hold a minimum of three years of experience leading data quality or governance initiatives in a consultative capacity. Two hands-on years championing Collibra Data Quality and Observability with customers are mandatory. A deep practical fluency in core data quality concepts is required. This includes quality dimensions, rule design, scoring, monitoring, alerting, remediation workflows, and data observability.

The ability to engage customers with credibility is critical for influencing both technical teams and executive leadership. Exceptional presentation and facilitation skills enable you to command a room of diverse stakeholders. Superior written and verbal communication skills must convey complex concepts clearly and persuasively. A bachelor's degree or equivalent related working experience is a prerequisite.

Demonstrated proficiency in leveraging AI tools such as Claude, Gemini, ChatGPT, or Copilot to solve real-world business challenges is necessary. This role is not eligible for visa sponsorship. You must excel at discovering use cases and reflecting on problems to propose effective solutions.

The ability to read and write SQL within the context of data quality rules is essential. You must recognize data outliers, duplicates, patterns, and valid value ranges. A comprehensive understanding of core data quality dimensions is required. You will craft logic for data quality rules, including cross-dataset comparisons and complex conditions.

Safe use of functions like `length` within rule logic is expected. You will work with GET and POST requests and handle boolean operators such as AND, OR, and NOT. Navigating Linux filesystems and command line tools is a basic expectation for this position.

Nice to have

A basic ability to read and write Python in a data context is a valuable asset.

Skills & tools

Proficiency with Collibra Data Quality, Collibra Observability, SQL, Python, REST APIs, and Git is expected.

Practical notes

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About the company

Collibra builds the control plane for enterprise AI, using deep ontology engineering to govern context and control across data, models, and agents. The platform supports AI leaders who need reliable structure while systems move quickly.

People design, implement, and operate governance workflows. They work with reference architectures, policy definitions, and integrations. The team maintains clarity, consistency, and traceability as organizations scale AI initiatives.