"*" indicates required fields 1Details2Part 13Part 24Part 35Qualifications, Prior Learning & Experience Welcome to the Corndel Data Engineering Level 5 Skills Radar. This skills radar is a self assessment tool that is used to confirm your eligibility for this apprenticeship, and to help us to understand your current level of knowledge, skills and behaviours against those of this programme. If you hold qualifications, or have prior learning or experience related to this programme we will contact you to discuss this further, as you may not be eligible for government funding. Unique IDHiddenDate of completion DD slash MM slash YYYY Your detailsName* First Last Aptem email address* Enter Email Confirm Email Employer* Job title* Your data will be processed by Corndel in accordance with their Privacy Policy.Privacy policy* Please tick here to confirm your consent* The statements below cover the knowledge, skills and behaviours that you will learn and develop on this programme. Using the 1-10 guidance below, please assess your current level of knowledge and experience in relation to the level of this programme. 1-2:   I have no prior learning or experience and cannot demonstrate this. 3-4:   I have some prior learning or experience and demonstrate this inconsistently. 5-6:   I have moderate prior learning or experience and demonstrate this occasionally. 7-8:   I have good, or very good prior learning or experience and demonstrate this often.      9:   I have extensive prior learning or experience and demonstrate this to an exemplary standard.   10:   I am an expert and have nothing further to learn. Part 1a*12345678910Uses of different on-demand cloud computing platformsApproved organisational architectures and the relevant data development frameworksTypes and uses of data engineering tools in organisations and how to apply themMaintaining a working knowledge of data use cases within organisationsData normalisation principles and the advantages achieved for data protection, redundancy and inconsistent dependencyDifferent types of data stores to monitor and optimise the performance of data productsPart 1b*12345678910Techniques such as star schemas, data lakes and data marts and the impact they have on data warehousing principlesWorking collaboratively with different stakeholders to develop and maintain working relationshipsMethods and techniques used to communicate messages about the data product that meet the needs of the audienceCollate, evaluate and refine user requirements to design and build a scalable data product that serves multiple needs and complies with regulatory requirementsInherent risks of data and how to ensure data qualityData quality metrics and their frameworks and tracks them to ensure quality, accuracy and reliability of the data product The statements below cover the knowledge, skills and behaviours that you will learn and develop on this programme. Using the 1-10 guidance below, please assess your current level of knowledge and experience in relation to the level of this programme. 1-2:   I have no prior learning or experience and cannot demonstrate this. 3-4:   I have some prior learning or experience and demonstrate this inconsistently. 5-6:   I have moderate prior learning or experience and demonstrate this occasionally. 7-8:   I have good, or very good prior learning or experience and demonstrate this often.      9:   I have extensive prior learning or experience and demonstrate this to an exemplary standard.   10:   I am an expert and have nothing further to learn. Part 2a*12345678910Tools and programming to query and manipulate data and implement automated validation checks, showing the methodologies used for moving data from one system to another for storage and handlingData ingestion frameworks such as streaming, batching and on demand services to move data from one location to another in order to optimise data ingestion processesSystematically clean, validate and describe data at all stages of extract, transform and load, showing how combining disparate data sources and taking different approaches to data integration delivers value to an organisationEvaluate opportunities to extract value from existing data products whilst applying the principles of data and considering costs, environmental impact and potential operating benefitsSecurity, scalability and governance when automating data pipelines using programming languages and data integration platforms with graphical user interfacesDeployment approaches for new data pipelines and automated processesPart 2b*12345678910Legislation associated with the use and collation of data, including concepts of data governance and regulatory requirementsData, information security standards, ethical practices and data management policies and procedures to ensure data systems are used securely and in accordance with relevant legislationHow debugging, version control and testing have an impact on software development and the principles for data productsSupport software development principles and advocate software development best practice when working with other data professionalsWorking with structured, semi-structured and unstructured data, developing algorithms to extract from sourcesTechnology and service management best practice The statements below cover the knowledge, skills and behaviours that you will learn and develop on this programme. Using the 1-10 guidance below, please assess your current level of knowledge and experience in relation to the level of this programme. 1-2:   I have no prior learning or experience and cannot demonstrate this. 3-4:   I have some prior learning or experience and demonstrate this inconsistently. 5-6:   I have moderate prior learning or experience and demonstrate this occasionally. 7-8:   I have good, or very good prior learning or experience and demonstrate this often.      9:   I have extensive prior learning or experience and demonstrate this to an exemplary standard.   10:   I am an expert and have nothing further to learn. Part 3a*12345678910Analysis of root cause investigation when responding to incidents within data processing pipelines, whilst troubleshooting and providing resolutions to stakeholdersIdentify and escalate risks and incidents, communicate downtime and issues with database access in line with policies in order to mitigate operational impact whilst ensuring business continuityStrengths and weaknesses of prototype data products to integrate within an organisation’s overarching data structure, taking into consideration the lifecycle of implementing data solutions in a businessProduce and maintain technical documentation for a data product in order to meet organisational user requirements, whilst adapting to changing work priorities to ensure that deadlines are metSustainable solutions in relation to data products and environmental social governance to ensure the use of less carbon across the various stages of product and service deliveryTake responsibility to identify new tools and technologies to optimise sustainable data products and services and recommend potential opportunities for use within organisationsPart 3b*12345678910Collate, evaluate and refine business requirements, to design, build and maintain a system whilst ensuring that organisational strategies for sustainable, net-zero technologies are consideredIdentify and remediate technical debt, assess for updates and obsolescence within their promotion of continuous improvement to the data system development processPrioritise sustainable outcomes and keep up to date with technological developments in data science, data engineering and AIIdentify and assess new technologies, as well as gaps in existing tools and technologies, that offer increased performance of data products and implementation of changes requiredPrinciples of descriptive, predictive and prescriptive analytics Have you previously achieved a qualification equivalent to a level 5 (e.g. foundation degree) or above?* Yes No Please list all relevant qualifications*Click on the (+) to add further qualificationsTitleLevel Add RemoveOther work experience Tick here if you have any other work experience that may be relevant to this apprenticeship that could affect your eligibility. Please provide details:* SignatureHiddenDate DD slash MM slash YYYY