Analytics Engineer

Responsibilities

• As the Staff Analytics Engineer for Cyber Analytics, you will set the technical direction for cybersecurity data products, ensuring they are trusted, scalable and built for impact.
• You will drive data modelling standards, analytics architecture and engineering best practices, enabling high-quality insights, AI-powered analytics and secure self-service capabilities.
• Partnering with security, platform and engineering teams, you will shape long-term strategy, champion data governance and AI responsibility, and mentor Analytics Engineers to deliver exceptional data products at scale.
• Technical Leadership and Data Product Strategy:
  Define and drive the technical direction for cybersecurity data products, establishing engineering standards, best practices and operating models that enable the team to deliver trusted, scalable and high-value data solutions aligned to security and business objectives.
• Analytics Architecture:
  Lead the design and evolution of the cybersecurity analytics architecture, defining data patterns, modelling approaches and data product frameworks that support reporting, advanced analytics, machine learning, GenAI and AI-powered analytics experiences at scale.
• Data Integration, Transformation and Quality:
  Establish the strategic approach for data integration, transformation and quality management across the raw, trusted and curated layers of the data ecosystem, ensuring data products are reliable, governed, reusable and fit for purpose.
• Data Modelling and Semantic Enablement:
  Define and govern data modelling standards and semantic layer design principles that enable consistent, discoverable and trusted data products, supporting self-service analytics, threat investigation and decision-making across security teams.
• Engineering Excellence and Documentation:
  Champion engineering excellence by driving coding standards, testing frameworks, peer review practices and documentation approaches that improve quality, maintainability, consistency and knowledge sharing across the Analytics Engineering function.
• Automation and Analytics Ops:
  Lead the adoption of automation and Data Ops practices that improve the deployment, testing, monitoring and operational management of data products, enhancing scalability, reliability and developer productivity.
• AI and Analytics Enablement:
  Shape the strategy and technical approach for analytics agents and conversational analytics capabilities, enabling users to explore and investigate security and business data through natural language experiences while ensuring responsible AI adoption through appropriate governance, security controls and human oversight.
• Data Governance, Security and Compliance:
  Establish and promote data governance, security and compliance standards across the cybersecurity analytics estate, ensuring sensitive data is protected and managed in accordance with organisational policies and regulatory requirements.
• Cross-functional Leadership and Influence:
  Partner with security, engineering and data leaders to shape roadmaps, influence architectural decisions and align analytics capabilities with strategic priorities, communicating complex technical concepts clearly to both technical and non-technical stakeholders.
• In addition to the above core accountabilities, I am also responsible for contributing to and supporting the recruitment, coaching, mentoring and development of Analytics Engineering talent, helping to raise technical capability and foster a culture of engineering excellence across the Cyber Analytics team.

• Strong passion for data engineering, data modelling, data quality and building trusted, scalable data products that enable analytics, machine learning and AI-driven use cases.
• Proven experience leading the design and delivery of enterprise-scale data products, defining technical strategy, architectural patterns and engineering standards while providing technical leadership across teams.
• Expert programming experience with Python/PySpark and advanced proficiency in SQL for large-scale data transformation, optimisation and analytics workloads.
• Extensive experience designing and implementing scalable data solutions on cloud platforms such as Databricks on Azure, including data lakehouse architectures, data modelling frameworks and data quality controls.
• Deep understanding of analytics architecture, data modelling methodologies, semantic layer design, and approaches for delivering discoverable, governed and reusable data products.
• Expertise in ETL and ELT frameworks for large-scale batch and near real-time processing, with hands-on experience using orchestration and transformation technologies such as Airflow and dbt.
• Strong knowledge of software engineering best practices, including coding standards, code reviews, testing strategies, version control systems such as Git, and CI/CD processes.
• Experience driving automation and Data Ops practices that improve deployment, monitoring, reliability and operational efficiency of data…