IT · Project
Data Management
Data management projects involve setting up the processes and systems an organisation uses to collect, store, and maintain its data. This type of project appears at organisations with data spread across many disconnected systems.
In this project you might define data ownership rules for a set of business-critical datasets, design a data catalogue that makes it easy to find and understand available data, or set up quality monitoring processes that flag problems before they reach downstream users.
The goal of a data management project is to make data reliable, findable, and well-governed across an organisation. Without this foundation, analysts spend too much time fixing data problems instead of analysing data, and different teams end up working from different versions of the same numbers. Good data management reduces that friction and makes all other data work more effective.
The daily work involves assessing the current state of data quality and governance, defining ownership and accountability for key datasets, and setting up processes or tools to maintain quality over time. You often interview business stakeholders to understand how they use data and what problems they encounter. The output is usually a combination of documentation, process design, and tooling recommendations.
The main tools are data cataloguing platforms, SQL for profiling and quality checks, and Python for automating monitoring. Knowledge of data governance frameworks and concepts like data lineage, data dictionaries, and metadata management is useful. The work connects to the Data Engineer and Data Analyst roles and appears most often in large organisations or as part of broader data transformation programmes.
Related roles
The roles most associated with Data Management projects.
Related sectors
Sectors where Data Management projects are most common.
Companies
Organisations working on Data Management projects where graduates typically contribute.
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