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- Develop, maintain, and optimize the D&A architecture on AWS and Azure, including the design, deployment, and maintenance of the cloud based Pladis’ data platform (PDP).
- Deliver an architecture that's globally scalable, agile, and supportive of digital services through Data and Insights as a service via APIs.
- Design and oversee the implementation of the PDP 2.0 Tech stack.
- Design and oversee data architecture to harmonise external, internal and Microsoft Graph data to deliver AI use cases
- Champion engineering standards and ensure new engineers' quick integration and productivity.
- Lead engineering problem solving and provide technical guidance to squad engineers.
- Foster a component based delivery approach to enhance reusability across different areas of the business.
- Collaborate with stakeholders to guarantee timely engineering deliverables and work with partners to accelerate delivery velocity within teams.
- Implement and review measures to track and enhance data engineering productivity.
- Implement end to end data security measures, including periodic penetration testing, audits, and assurance of PDP.
- Coordinate with the CISO, DPO, and other teams to ensure data security, GDPR compliance, and overall data assurance.
- Initiate and oversee a continuous data quality improvement strategy both at PDP and in source systems.
- Set and align the AI architecture vision with the company’s overarching business goals.
- Stay updated with latest AI and ML trends to keep the company at the technological forefront.
- Lay down AI architectural standards, best practices, and guidelines for system design.
- Design AI solutions that are robust, scalable, and in line with business requirements.
- Integrate generalized AI models into business processes and ensure they harmonize with existing systems.
- Manage and safeguard data for AI models, emphasizing governance, quality, security, and accessibility.
- Design storage solutions optimized for real time processing, querying, and scalability.
- Collaborate with data teams to streamline AI model lifecycle processes.
- Mentor and guide AI teams, ensuring alignment with business objectives.
- Engage regularly with business stakeholders to align AI outputs with business needs.
- Encourage AI literacy within the company through training and engagement.
- Ensure smooth deployment, monitoring, and maintenance of AI models in production environments.
- Uphold ethical standards and ensure compliance with data privacy regulations.
- AWS (Amazon Web Services): Knowledge of services like S3, EC2, Lambda, RDS, Redshift, EMR, SageMaker, Glue, and Kinesis.
- Azure: Proficiency in services like Azure Blob Storage, Azure Data Lake, VMs, Azure Functions, Azure SQL Database, HDInsight, and Azure Machine Learning Studio.
- SQL & NoSQL Databases: Experience with databases like PostgreSQL, MySQL, MongoDB, and Cassandra.
- Big Data Ecosystems: Hadoop, Spark, Hive, and HBase.
- Data Pipelining Tools: Apache NiFi, Apache Kafka, and Apache Flink.
- ETL Tools: AWS Glue, Azure Data Factory, Talend, and Apache Airflow.
- Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, and MXNet.
- AI Services: AWS SageMaker, Azure Machine Learning, Google AI Platform.
- Containerization: Docker and Kubernetes.
- Infrastructure Automation: Terraform, Ansible, and AWS CloudFormation.
- API Development: RESTful API design and GraphQL.
- Microservices Tools: Istio, Envoy, and Linkerd.
- Identity & Access Management: AWS IAM, Azure Active Directory.
- Data Governance Tools: AWS Lake Formation, Azure Purview.
- Data Security Tools: AWS Key Management Service (KMS), Azure Key Vault.
- Visualization Tools: Tableau, Power BI, Looker, and Grafana.
- Analytics Services: AWS Athena, Amazon QuickSight, Azure Stream Analytics.
- Version Control: Git (and platforms like GitHub, GitLab).
- CI/CD Tools: Jenkins, Travis CI, AWS CodePipeline, Azure DevOps.
- IaC (Infrastructure as Code): Mastery in automating infrastructure setup.
- Serverless Architectures: Experience with AWS Lambda, Azure Functions.
- Edge Computing: Knowledge of AWS Greengrass, Azure IoT Edge.
- Networking & Content Delivery: Experience with VPCs, CDN solutions like AWS CloudFront, and Azure Content Delivery Network.
We will not accept CVs from any other sources other than those currently on our PSL. We will not pay a fee for any candidate that has not been represented by a provider on our PSL.