Overview The Data Expert plays a central role in making organizational data understandable, discoverable, and usable for advanced analytics and Artificial Intelligence initiatives. Sitting within the Data team, the role bridges the AI team, data engineering teams, and the broader data landscape of the organization. The Data Expert develops a deep understanding of available data assets across the data platform, operational systems, and relevant external sources. Based on this knowledge, the Data Expert helps shape analytical and AI-driven initiatives by assessing the availability and suitability of data and translating use case needs into clear and actionable data requirements. Rather than implementing pipelines or models directly, the Data Expert shapes what gets built by defining datasets, identifying gaps in the data landscape, and ensuring data can be effectively used for analytics and AI solutions. Key Responsibilities Understand and Map the Data Landscape • Develop and maintain a broad understanding of available data assets across the organization, including data on the data platform, operational systems, and external sources. • Ensures documentation of key datasets, definitions, and structural characteristics so they can be easily discovered and understood by data consumers. • Maintain a structured overview of data domains and datasets to support analytics and AI initiatives. • Act as a reference point for teams seeking to understand available data and how it can be used. Assess Data Suitability for Analytics and AI • Participate in the shaping of analytics and AI initiatives to determine whether suitable data exists to support the proposed use case. • Evaluate datasets for their suitability in analytical and AI contexts, including aspects such as coverage, completeness, historical depth, granularity, and potential bias. • Identify data gaps that could block initiatives and recommend mitigation strategies such as additional data collection or enrichment through external sources. • Provide clear assessments of data feasibility to support informed decision-making. Translate Use Cases into Data Specifications • Translate analytical and AI use cases into clear and implementable data requirements for engineering teams. • Define dataset structure and characteristics required for the use case, including: – entities and dataset grain – required attributes and features – historical depth and refresh frequency – data transformations and enrichment needs • Write precise functional and non-functional data requirements that engineers can implement without ambiguity. • Validate delivered datasets against the original requirements to ensure they meet the intended analytical or AI needs. Own the Data Product Backlog • Maintain and prioritize a backlog of data products and datasets required to support analytics and AI initiatives. • Balance stakeholder demand with engineering capacity to determine which data products should be developed first. • Track delivery progress and ensure datasets remain usable and relevant after release. • Continuously refine data product definitions based on evolving analytical and AI needs. Source and Evaluate External Data • Identify external datasets that could enrich internal data and unlock new analytical or AI capabilities. • Evaluate external data sources for quality, relevance, coverage, licensing conditions, and fitness for purpose. • Coordinate with engineering teams to integrate relevant external datasets into the data platform. Enable Data Consumers • Ensure that data consumers, including AI practitioners, analysts, and business teams, can easily find, understand, and use relevant datasets. • Provide guidance on the meaning, structure, and limitations of available data. • Proactively signal data limitations, quality concerns, or structural constraints that could affect analytical outcomes. • Promote transparency and shared understanding of the organization’s data assets. Required Profile • Proven experience in a data-focused role such as data analyst, analytics engineer, data steward, or data specialist. • Strong understanding of data platforms, data modeling concepts, and common data formats. • Experience translating analytical or data-driven use cases into clear and implementable data specifications. • Ability to explore and analyze datasets independently, typically using tools such as SQL or equivalent data exploration capabilities. • Working knowledge of analytics and AI concepts sufficient to understand how data is used in analytical models and machine learning solutions. • Experience working with cross-functional teams including data engineers, analysts, AI practitioners, and business stakeholders. • Ability to work independently, manage a backlog of work, and take ownership of outcomes. • Strong communication skills with the ability to explain data concepts clearly to both technical and non-technical audiences. Key Competencies • Enterprise data landscape knowledge • Data discovery and evaluation • Analytics and AI data readiness • Data specification and requirements definition • Backlog and product ownership • Cross-team collaboration • Documentation and knowledge sharing • Independent judgment and prioritization