As the Analytics Engineering Manager in the cyber data team, you will play a crucial role in the development, management, and optimisation of security data products. You will lead technical decisions and be responsible for a team of analytics engineers to ensure the delivery of high-quality data products needed for security teams operations, analytics, machine learning and Gen AI applications. The Analytics Engineering team plays a direct role in the success of the Cyber Data Intelligence team and is a key driver in promoting a data-centric culture within the security function. You will also help shape how AI and conversational analytics are used to make security data more accessible, enabling users to explore data, generate insights and answer questions using natural language rather than relying solely on traditional SQL, reports and dashboards. The above information is a shortened summary, refer to our policies for full details.
You will be responsible for Following our Business Code of Conduct and always acting with integrity and have these specific risk responsibilities:
Data Transformation and Modelling:
Transforming raw data into various data models by cleaning, normalising, standardising, filtering, aggregating, and formatting the data adhering to Open Cybersecurity Schema Framework standards.
Data Analysis:
Support a variety of data analysis, involving the exploration and analysis of source, trusted, and curated security data to identify patterns, trends, and insights in collaboration with security analysts.
Data Quality and Documentations:
Maintaining the data quality across the security lake (silver, gold and reporting layers) by implementing various validation and quality measures. Collaborate with source data owners, security analysts, and engineers to manage the data catalog, lineage, data dictionary, and data transformation processes.
AI-enabled and Conversational Analytics:
Enable security teams to interact with and analyse data using natural language and AI, reducing the reliance on writing SQL or building traditional reports and to support data exploration, insight
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