Introduction
For one of our fast growing practices, we are looking for a Data Quality Analyst. If the following questions spark your curiosity, this role might be exactly what you’re looking for:
How do you define “good data quality” when working with unstructured data such as text and images?
How do you approach the challenge of data quality for “AI” versus “AI for data quality”?
What are the biggest blind spots organizations face when measuring data quality?
How would you design a data quality framework for LLM-based applications?
Case: A company wants to deploy an AI assistant or chatbot on top of poorly documented and inconsistent internal data—how would you approach this in your first 90 days?
Your Role
This is a primarily advisory position, focused on enabling rather than executing. While you’re comfortable stepping into hands-on data challenges when needed, the role is not designed as a traditional staffing position embedded in day-to-day operational work.
Instead, you support clients in building the right frameworks, processes, and capabilities, empowering their teams to independently manage and improve their data quality landscape.
This is neither a pure data analysis nor data engineering role, and it goes beyond simply executing assigned tasks. You are expected to proactively identify opportunities and challenges, guide improvements, and provide clear direction in a fast-evolving data environment.
In short: you create tangible, lasting impact.
Your responsibilities
Develop and implement data quality standards, communicate data (quality) principles fit for the specific client context and embed them in the client’s daily operating rhythm to improve trust in both business and technical data domains.
Advice clients on tool and technology improvements in function of the client’s maturity, technology stack and needs.
Support clients in their day-to-day activities by bridging the gap between business and technology on topics like data quality business needs, monitoring and remediation processes; Train them on what good data quality looks like and inspire them with best practices and tooling knowledge.
Identify opportunities to create more impact with the available tool stack, launch experiments to optimize workflows and collaborate with client teams to improve process automation accordingly.
Help clients to internally promote its data quality tooling and refine its value proposition. Increase the adoption of the DQ tooling by supporting teams on its implementation in key use cases.