Description:
We ideally need you to have most of the following on your CV:
- 7+ years’ experience in designing and implementing modern data, analytics, and AI programs, with a focus on scalable, cloud-native solutions for large, complex organisations.
- Expertise in architecting data platforms , including data lakes, data lakehouses, data meshes, and real-time streaming architectures, to support advanced analytics, AI, and business intelligence use cases.
- Proven experience in designing architectures for structured, semi-structured, and unstructured data, leveraging technologies like Databricks, Snowflake, Apache Kafka, and Delta Lake to enable seamless data processing and analytics.
- Hands-on experience in data integration , including designing and optimising data pipelines (batch and streaming) and integrating cloud-based platforms (e.g., Azure Synapse, AWS Redshift, Google BigQuery) with legacy systems, ensuring performance and scalability.
- Deep knowledge of ETL/ELT processes , leveraging tools like Apache Airflow, dbt, or Informatica, with a focus on ensuring data quality, lineage, and integrity across the data lifecycle.
- Practical expertise in data and AI governance , including implementing frameworks for data privacy, ethical AI, and compliance with regulations such as GDPR, EU AI Act, and CSRD, using tools like Azure Purview, Collibra, or OneTrust.
- Strong experience in data security , including designing zero-trust architectures, encryption protocols, and access controls (e.g., RBAC, ABAC) to secure data platforms and AI models.
- Proven track record in developing technical blueprints for Generative AI, Large Language Models (LLMs), and advanced analytics platforms, including MLOps pipelines (e.g., MLflow, Kubeflow) and integration with business workflows.
- Experience in aligning data and AI strategies with business objectives, including designing solutions for real-time analytics, predictive modelling, intelligent automation, and synthetic data generation for privacy-preserving analytics.
- Expertise in integrating AI models into enterprise systems, leveraging APIs, containerisation (Docker, Kubernetes), and serverless architectures to enable scalable deployment.
- Proactive evaluation of emerging technologies , including edge AI, federated learning, quantum-inspired data processing, and AI-driven data orchestration, to drive innovation in client solutions.
- Curiosity and commitment to staying ahead of trends in data and AI, such as data fabric, automated machine learning (AutoML), and low-code/no-code analytics platforms, with familiarity in vendor ecosystems (e.g., Microsoft, AWS, Google, Databricks).
- Exceptional stakeholder management and presentation skills , with the ability to translate complex data and AI concepts into actionable insights for non-technical audiences, including C-suite executives.
- Experience leading cross-functional teams in delivering data and AI projects, ensuring timely and budget-conscious execution, while mentoring junior team members.
- Relevant certifications , such as AWS Certified Solutions Architect, Azure Data Engineer Associate, Google Professional Data Engineer, Databricks Certified Data Engineer, or TOGAF, with additional certifications in AI or MLOps being a plus.
Your Responsibilities
Your responsibilities and achievements will evolve as you grow with us. Initially, you can expect to:
- Advise clients on designing and implementing modern data and AI platforms that leverage trends like data lakehouses, real-time streaming, and Generative AI to deliver business value.
- Define and deliver data and AI solution architectures, including operating models that integrate cloud-native, hybrid, and edge-based technologies.
- Design and implement robust data governance and security frameworks, ensuring compliance with global regulations and fostering trust in AI-driven solutions.
- Architect scalable, high-performance data platforms and AI solutions, enabling use cases such as real-time decision-making, personalised customer experiences, and automated insights.
- Guide clients in transitioning from legacy systems to cloud-native and hybrid architectures, optimising data pipelines and AI model deployment for performance and cost-efficiency.
- Champion emerging technologies, such as AI-driven data orchestration, federated learning, and sustainable AI practices, to position clients at the forefront of innovation.
- Lead and mentor technical teams, fostering a culture of innovation, collaboration, and continuous learning.