Data Engineer

Norton Rose Fulbright; Newcastle upon Tyne; On-site; GBP 50,000 - 70,000; Full time

Job summary

A top international law firm in Newcastle upon Tyne is looking for a Data Management professional to handle data acquisition, pipeline development, and enhance decision-making through machine learning solutions. You will work closely with various stakeholders, ensuring data quality and compliance while collaborating on diverse data projects. Ideal candidates will have a relevant degree, strong problem-solving skills, experience in Azure technologies, and proficiency in data management tools. This role offers an opportunity to make a significant impact within the firm.

Qualifications

• Degree qualified in computer science, data analysis or related field.
• Experience in data integration, modeling, and quality.
• Proficiency in data science languages like Python or R.

Responsibilities

• Source, create, and clean data for operational use.
• Build and productionize ML/NLP solutions.
• Automate scalable data pipelines using Azure.
• Ensure data quality and compliance across the lifecycle.

Skills

Data management disciplines

Problem-solving skills

Collaboration skills

Proficiency in Azure/Microsoft Fabric

Proficiency in Python

Education

Degree in computer science or data analysis

Tools

SQL Server

Databricks

Apache Kafka

MongoDB

Job description

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)