About Ascent Flight Training

Ascent Flight Training is a joint venture between Lockheed Martin and Babcock International, responsible for delivering the UK Military Flying Training System (UKMFTS).  We train the next generation of Royal Air Force, Royal Navy, and Army pilots, using advanced aircraft, simulators, and training systems.

Role Purpose:

Design and deliver robust, scalable data systems and infrastructure that power the organisation’s analytics, AI, and decision-making capabilities.

By enabling governed data integration, transformation, and storage, this role ensures teams across the business have timely access to trusted, high-quality data, to unlock strategic insights, drive efficiency and support business growth.

The role will support strategic programmes by engineering trusted, governed data foundations that enable performance dashboards, predictive analytics, end-to-end training insight, and integration between Ascent data services, supplier-developed solutions and customer requirements.

Accountabilities:

Data Pipeline & Model Development: Design, implement, and maintain a data foundation for business analytics.  Implement workflows to ingest data from diverse sources, transform it for analytical use, and load it into data warehouses, data lakes, or marts.  For strategic programmes, support ongoing data capture, outcome tracking and repeatable analytical datasets that enable evidence-led decision-making.

Data Engine & Dashboard Integration: Configure, maintain and optimise Ascent data-engine capabilities to import, transform, validate and export data in agreed formats, enabling integration with internal and supplier-developed dashboards, analytical tools and programme reporting outputs.

Performance Monitoring & Optimisation: Continuously monitor data infrastructure, pipelines, and data models to identify bottlenecks and optimise performance, scalability, and reliability of data solutions.

Data Governance & Quality Assurance:  Ensure robust data governance practices are in place, including data lineage, metadata management, and adherence to data privacy and compliance standards.

Continuous Improvement: Stay current with emerging data engineering tools, cloud platforms, and best practices. Proactively identify opportunities to improve data workflows, reduce latency, and enhance the overall efficiency of data operations.

Stakeholder Collaboration:  Partner with cross-functional teams, including data analysts, business stakeholders, and ICT to understand data requirements, communicate solutions clearly, and ensure alignment to business objectives.

Skills, Knowledge & Experience Required:

SQL and Database Management: 

Essential experience required:  Advanced expertise in SQL for querying, transforming, and managing data in relational databases. Skilled in database design, performance optimisation, and maintaining data integrity across new and existing database solutions.  Knowledge of SSIS.

Data Warehouse Concepts:

Essential experience required: Knowledge and understanding of data warehousing principles and methodologies e.g. Kimball. Including dimensional modelling, star and snowflake schemas, incremental extracts and data integration techniques.

Desirable experience:  Knowledge of data warehousing tools and technologies such as Snowflake, Databricks, Azure Synapse.

Programming and Scripting:
Essential experience required:  Knowledge and understanding of programming languages such as Python, Spark, DAX, C# or Java for data manipulation, transformation, and automation tasks.  Azure Dev Ops / Git Hub / Visual Studio for code repositories, change management.

Desirable experience: Knowledge of scripting languages like PowerShell for process automation.  

Data Governance, Security/Privacy & Ethics:
Essential experience required: Strong understanding of data governance principles, data security, privacy regulations (including GDPR), and ethical data practices. Experienced in implementing access controls, encryption, and ensuring compliance with data protection standards.

ETL (Extract, Transform, Load):
Essential experience required: Practical experience designing and implementing ETL processes to ensure reliable, accurate data for analysis. Skilled in extracting data from diverse sources, transforming it for usability, and loading it into data warehouses or marts. Proficient with data pipeline tools such as SSIS and Azure Data Factory.

Appreciation of AI methodologies:

Essential experience required: Understanding of how engineered datasets support predictive modelling, statistical analysis, machine learning and dashboard-based decision support. Able to work with analysts, data scientists and suppliers to prepare governed, validated datasets for model development, testing, deployment and monitoring.

Desirable experience:  Knowledge and understanding of Data Science applications such as Machine Learning and Large Language Models.  Experience of working in an environment that utilised these methodologies. Familiarisation with AI models to assist workload, e.g. CoPilot.

This is a permanent role and provides a hybrid working pattern with 2 days in the office.

This role requires the successful candidate to obtain Security Clearance Check (SC).

We’re proud to foster a flexible and inclusive workplace where diverse perspectives are valued. Whether working onsite or remotely, we support a culture that encourages collaboration, individuality and work-life balance. We welcome applications from all backgrounds and are committed to building a diverse and inclusive workforce. We are open to considering flexible working arrangements for all roles, including part-time hours, job share, and alternative working patterns, and we encourage conversations about individual needs during the recruitment process. We are actively working to remove barriers within our hiring practices and aim to provide a fair, accessible and supportive experience for every candidate. If you require any adjustments at any stage of the recruitment process, please let us know and we will be happy to support you.