Senior Engineer - Data Analytics & Applications (Asset Management)

About the role

LTA is hiring a Senior Engineer (Manager / Senior / Executive level) in Data Analytics & Applications, within its Asset Management team. You will design and build data pipelines that prepare and optimise asset-related data for analytical applications, and help develop analytics solutions that turn data into insights for rail asset management. The role involves close collaboration with LTA teams, rail operators and system manufacturers.

Key responsibilities

• Design, develop and maintain data pipelines that collect, process and transform asset data from many sources for engineering and analytics.
• Ensure data integrity, quality and accessibility through sound data engineering and management practices.
• Work with stakeholders to turn their data needs into technical specifications for data engineering and analytics projects.
• Analyse data to find trends and patterns that inform asset management decisions.
• Develop data models and analytics frameworks that support business objectives and asset performance.
• Create dashboards and visualisations that communicate asset performance metrics to stakeholders.
• Manage data in storage solutions such as databases and data lakes.
• Help prioritise and deliver analytics user stories with cross-functional teams.
• Document data flows and analytics workflows, and stay current with industry tools and best practices.

Required profile

• Degree in Data Science, Computer Science, Engineering or a related field.
• Experience in data engineering and data analytics; railway engineering or a similar role is an advantage.
• Strong analytical and problem-solving skills, with a focus on data quality and performance.
• Excellent written and verbal communication, able to explain technical concepts to non-technical people.
• Ability to work independently and in a team, with a proactive approach to using data to improve asset management.

Required skills

• Data engineering tools, programming languages and data modelling (e.g. Python, SQL, ETL tools)
• Analytics tools (e.g. Tableau, Power BI, LLMs, machine learning models)