Overview
We are looking for a highly capable Data Analyst / Data Modeler contractor to support a major enterprise data transformation programme, the Data Integration Hub (DIH).
This role sits within a high-performing Data Architecture & Engineering function and will play a key part in delivering clean, reconciled, and business-ready data into our analytical platforms.
You will work at the heart of the programme, bridging business requirements, data modelling, and engineering delivery.
Key Responsibilities
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Analyse complex datasets to understand structures, relationships, and data quality issues
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Define source-to-target mappings and detailed transformation logic
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Develop and maintain conceptual, logical, and physical data models
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Translate requirements into clear pseudo-logic and implementation-ready specifications
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Perform data reconciliation and variance analysis between legacy and new platforms
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Identify and resolve data issues through structured root cause analysis
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Support testing and validation activities during key delivery and go-live phases
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Work closely with Data Engineers, Architects, and business stakeholders to ensure delivery of high-quality data
Skills & Experience
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Strong experience in data modelling (conceptual, logical, physical)
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Proven capability in data analysis and source-to-target mapping
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Advanced SQL skills for profiling, validation, and complex queries
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Experience defining transformation logic / pseudo-code for data pipelines
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Good understanding of data warehousing and ETL/ELT processes
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Strong problem-solving and data debugging skills
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Experience working in complex, multi-system data environments
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Knowledge of PySpark, SparkSQL advantageous to the role
What We’re Looking For
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A hands-on, detail-oriented data specialist who thrives in complex environments
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Someone who can bridge business and engineering with clarity and structure
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Strong communicator, comfortable working with distributed teams within Data Architecture & Engineering function and across external functions
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Experience in data reconciliation, quality, and governance-driven programmes is highly desirable
