take responsibility for driving, designing and building scalable ETL systems for a big data warehouse support high performing ML algorithms, predictive models and support real-time data visualisation requirements kept up to date Conduct data design, database architecture, metadata and repository creation Translates business needs into long-term architecture solutions. Define, design and build dimensional databases. Responsible in Python or R or willingness to learn. SAP architecture, implementation and operations Trends across
structured and unstructured data sets. Responsibilities: Model complex business problems, discovering insights intelligent automation and predictive modelling. Build machine learning models from and utilises distributed Kafka. Provide input into Data management and modelling infrastructure requirements and adhere to the Ensure business integration through integrating model outputs into end-point production systems, are understood Measure proficiency in using the diagramming and modelling techniques vital for requirements analyses. Qualifications
commercial Java coding experience, MicroServices Architecture and Cloud knowledge. The development landscape JavaScript Microservices Architectures Cloud Architectures Container Architectures Docker Microsoft Azure HTML 5 CSS Git Maven or Jenkins Architecture Interface Design Data modeling Database technologies Implementation
world to develop world class technology products, build great technology teams, generate more revenue, and solutions, including data visualization, data modelling, ETL processes, and data warehousing Design, implement deliveries Participate and contribute to the architecture design process Participate in reports design
will be responsible for driving, designing and building scalable ETL systems for a big data warehouse support high performing ML algorithms, predictive models and support real-time data visualisation requirements In-depth knowledge and experience of Retail SAP architecture, implementation and operations and strong networks
data architecture across several application platforms Analyse data elements and systems Build required extraction, transformation and loading of data Build, create, manage and optimise data pipelines Create Create data tooling, enabling data consumers in building and optimising data consumption Execute on the to understand viable data solutions within architectural guidelines Qualifications and Experience: Degree information technology 5-7 years' experience in building databases, warehouses, reporting and data integration
Solutions Contribute to the overall data warehouse architecture and data base designs Maintain and oversee the or B.Sc. (Informatics) - Essential Dimensional modelling and/or relevant Microsoft certification – Advantageous Proven data modelling techniques (3 years) Experience in Ralph Kimball data warehouse modelling (3 years)
machine learning, statistical and mathematical models to solve business problems and enable the effective technologies and model data preparation. Responsibilities: Build statistical models Manage the development Define strategy to enable business use of predictive models Manage controls to enable risk monitoring Implement validate the effectiveness of the models Monitoring/Calibration of models Effective self-management and teamwork
specifications and designing, building and deploying BI solutions (e.g., data warehousing models) and creating visualizations reports for requested projects . Responsibilities: Build enterprise scalable, complex data visualizations in SQL, SSRS, Excel Work with the team in data modelling, database design, creating efficient SQL for fast Production experience working with at least modern modelling tools - on premise and / or in the cloud (e.g Analytics) Production experience working with DAX modelling measures (e.g. Power BI, Analysis Services, DAX
for a Senior Data Analyst whose focus will be on building out our processes and writing superb SQL. In addition candidate will be responsible for maintaining and building out our reporting DB You must be e willing to to develop data models and pipelines for research, reporting, and machine learning Model front-end and the system and to enable powerful data analysis. Build data pipelines that clean, transform, and aggregate analysts and BI analysts for reporting. Develop models that can be used to make predictions and answer