Collibra Accounting Support Specialist at Collibra focused on Data Quality to help customers build trustworthy data programs. This role provides strategic advisory, facilitates workshops, and drives continuous improvement in data quality.
Responsibilities
Provide strategic advisory to executive stakeholders on Data Quality programs
Advise on rules, monitoring, and observability for data quality
Help establish remediation and continuous improvement workflows
Guide integration of data quality into pipelines and governance workflows
Design and lead customer workshops on data quality topics
Execute Data Quality Maturity Assessments and formulate roadmaps
Develop and share data quality best practices and success stories
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.).
