Data Analytics & AI Engineering Manager

About the role

Lead the design, development, and operationalisation of data platforms, advanced analytics, and AI solutions within the ADAS organisation. This strategic role bridges data engineering, analytics, and AI innovation to enable data-driven decision-making, automation, and scalable AI capabilities across business units. You will align capability development with enterprise architecture, innovation roadmaps, project delivery, and managed services, including L2/L3 support.

Key responsibilities

• Define and execute the Data, Analytics and AI capability roadmap aligned with strategic goals.

• Establish capability maturity models and identify business opportunities that leverage data and AI.

• Lead the development of modern data platforms, including data lakes, warehouses, and lakehouse architectures.

• Oversee scalable and secure batch and real-time data pipelines, system integration, data governance, data quality, and metadata management.

• Deliver descriptive, diagnostic, predictive, and prescriptive analytics solutions and standardise BI dashboards and reporting frameworks.

• Enable self-service analytics and translate business requirements into data models, insights, and actionable recommendations.

• Design and deploy machine learning and AI solutions across forecasting, natural language processing, computer vision, and automation use cases.

• Operationalise AI through MLOps and DevOps practices, including model CI/CD, governance, explainability, and ethical AI controls.

• Establish L2/L3 support models, service levels, performance metrics, rate cards, and service packages for data and AI services.

• Partner with business leaders to identify high-value use cases and support executive decision-making, pre-sales, and solution design.

• Build and lead multidisciplinary teams across data analysis, engineering, BI, visualisation, data science, AI, governance, and quality.

• Define roles and career pathways while fostering continuous learning, innovation, experimentation, and quality delivery.

What we're looking for

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.

• 8–12 or more years of experience in data engineering, analytics, or AI roles, including 3–5 or more years in leadership or management.

• Experience with enterprise-scale data platforms, AI deployments, and managed services delivery.

• Strong technical expertise in SQL, Python, Spark, ETL/ELT tools, and cloud platforms such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform.

• Experience with Power BI, Tableau, TensorFlow, PyTorch, Scikit-learn, Snowflake, Databricks, and Hadoop.

• Knowledge of DAMA-DMBOK data governance, MLOps, DevOps, Agile or Scrum delivery, and TOGAF-aligned architecture practices.

• Strategic thinking, roadmap development, stakeholder management, communication, financial awareness, and cost optimisation skills.

• Ability to lead multidisciplinary teams, translate between technical and business stakeholders, and contribute to pre-sales and solution design.