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Data Engineer

Norton Rose Fulbright
Newcastle upon Tyne, United Kingdom
7 months ago
Applications closed

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Posted
8 Feb 2026 (7 months ago)
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Responsibilities
  • Data Acquisition & Preparation: Source, create, collect, clean, transform, and structure data for analysis and operational use, including synthetic data generation.
  • Build and productionise ML/NLP solutions for legal and business use cases to enable better decision making.
  • Feature Engineering & Integration: Develop new features, integrate data from internal/external sources, and create unified views to enhance performance and outcomes.
  • Pipeline Development & Automation: Build, optimise, and automate scalable data pipelines using tools like Azure, Fabric, and Databricks; develop reusable components and templates.
  • Governance, Quality & Security: Ensure data quality, governance, classification, retention, and robust security measures across the data lifecycle, supporting compliance and auditability. Contribute to standards, code reviews and communities of practice.
  • Metadata & Semantic Services: Provide metadata management, define categories and relationships, and enable semantic capabilities for data discovery and interoperability.
  • Monitoring & Issue Resolution: Monitor health and performance of data systems, identify and resolve quality issues, bottlenecks, anomalies, and validate data across environments.
  • Analytics & Machine Learning Support: Apply analytics techniques, visualise data, prepare and serve data for machine learning, and collaborate with data scientists to operationalise models.
  • Collaboration, Documentation & Standards: Work with stakeholders to deliver scalable data products, implement DataOps, telemetry, quality checks and CI/CD practices, maintain documentation, and promote adherence to architecture standards.
Qualifications
  • Ideally degree educated in computer science, data analysis or similar
  • Strategic and operational decision-making skills
  • Ability and attitude towards investigating and sharing new technologies
  • Ability to work within a team and share knowledge
  • Ability to collaborate within and across teams of different technical knowledge to support delivery to end users
  • Problem-solving skills, including debugging skills, and the ability to recognise and solve repetitive problems and root cause analysis
  • Ability to describe business use cases, data sources, management concepts, and analytical approaches
  • Experience in data management disciplines, including data integration, modeling, optimisation, data quality and Master Data Management
  • Excellent business acumen and interpersonal skills; able to work across business lines at all levels to influence and effect change to achieve common goals.
  • Proficiency in the design and implementation of modern data architectures (ideally Azure / Microsoft Fabric / Data Factory) and modern data warehouse technologies (Databricks, Snowflake)
  • Experience with database technologies such as RDBMS (SQL Server, Oracle) or NoSQL (MongoDB)
  • Knowledge in Apache technologies such as Spark, Kafka and Airflow to build scalable and efficient data pipelines
  • Ability to design, build, and deploy data solutions that explore, capture, transform, and utilise data to support AI, ML, and BI
  • Proficiency in data science languages / tools such as R, Python, SAS
  • Awareness of ITIL (Incident, Change, Problem management)


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