Collibra Senior AI Engineer, Unstructured AI at Collibra focused on Data Quality programs. Responsible for strategic advisory, data quality rules design, remediation workflows, and customer enablement.
Responsibilities
Ship complex systems under real ambiguity, defining scope and acceptance criteria
Write and review production-grade backend code using Python and FastAPI
Build and deploy document-processing systems handling large-scale unstructured data
Integrate data from diverse enterprise sources such as SharePoint, Salesforce, and internal APIs
Partner across engineering, product, and sales teams to ensure alignment from prototype to rollout
Occasionally work with modern frontend development
Qualification
Minimum of 2 years of directUnderstanding of data outliers
Required
Minimum of 3 years of experience as a Data Quality Program Leader, Data Reliability/Governance Program Manager, or equivalent senior-level role.
Minimum of 2 years of direct, hands-on experience as a key Champion or Stakeholder of Collibra Data Quality & Observability.
Practical fluency in core data quality concepts, quality dimensions, rule design, DQ scoring, monitoring and alerting, data observability, and remediation workflows.
Proven ability to engage customers in a highly consultative manner, establishing immediate credibility and fostering long-term strategic relationships.
Exceptional presentation and facilitation skills, with the ability to command and influence a room of diverse stakeholders, from technical teams to executive leadership.
Superior written and verbal communication skills, capable of articulating complex concepts clearly and persuasively.
A bachelor’s degree or equivalent related working experience is required.
Demonstrated proficiency in leveraging AI tools (e.g., Claude, Gemini, ChatGPT, Copilot) to solve real-world business challenges, drive measurable outcomes, or streamline workflows.
This position is not eligible for visa sponsorship.
Strong ability to discover and work with use cases to help customers implement solutions. This process requires reflective listening, capturing the essence of the problem, and offering a proposed solution.
Ability to read and write Structured Query Language (“SQL”) in the context of DQ rules.
Understanding of data outliers, patterns, duplicates, and valid values (ranges, etc.).
