Plans and leads data engineering activities for strategic, large and complex analytics programmes.
NCF Category: Data Science
Data sources (Level Five)
Sets direction, ensuring that analytical work is enabled by appropriate, effective and ethical data engineering, modelling, techniques and tools, drawing on deep understanding of industry trends and emerging technologies.
Data quality (Data Science) (Level Five)
Develops organisational policies, standards, and guidelines for the secure and resilient operation of data analysis services and products.
Data structures (Level Five)
Works across the NHS and externally to identify opportunities to exploit the value of data sharing taking account of legal and ethical considerations.
Data standards (Level Five)
Develops organisational policies, standards, and guidelines for data engineering and modelling for analysis, aligned with ethical principles.
Accessibility (Level Five)
Takes responsibility for the secure and resilient operation of data analysis services and products.
Programming (Data Science) (Level Five)
Ensures that programming work is co-ordinated across projects and is accountable for overall quality and performance of software developed in area of responsibility.
Development approaches (Level Five)
Co-ordinates allocation of resource across development teams, ensuring right skills are available and utilised.
Engineering standards (Level Five)
Develops organisational policies, standards, and guidelines for software construction and refactoring, and for reproducible analytical pipeline principles. Takes account of departmental and cross government best practice / guidance adopting where relevant and influencing direction of these.
Development reviews (Level Five)
Sets long term strategy and plans work to ensure long-term sustainability and flexibility of products and minimise risk during development.
Development standards (Data Science) (Level Five)
Assesses risk and opportunity of emerging technology and practices and influences development of strategic technology capabilities and processes to enable effective and efficient delivery of data product development.
Automation (Level Five)
Dependencies (Level Five)
Academic/Industry links (Level Five)
Opportunities (Level Five)
Leads development of a culture that encourages innovation, risk taking and collaboration.
Evolving technology (Level Five)
Leads and plans the development of innovation capabilities and the implementation of innovation processes, tools and frameworks.
Emerging technology (Level Five)
Leads the communication of an open flow of creative ideas between interested parties and the set-up of innovation networks and communities.
Experimentation (Level Five)
Plans and leads strategic large and complex business process improvement activities through automation.
Emerging technology selection (Level Five)
Directs the identification, evaluation and adoption of new or existing data technologies to improve business processes.
Adoption (Data Science) (Level Five)
Exploits existing or new data technologies and ensures adoption with adherence to policies and standards.
Alternative solutions (Level Five)
Engages at a strategic level with external stakeholders/suppliers to identify opportunities, influencing the direction of travel in partner organisations for mutual benefit.
Proof of concept (Level Five)
Takes strategic view identifying long term opportunities and ensures the organisation is well placed to take advantage of cutting-edge developments.
Innovation strategy (Level Five)
Directs the strategy for data science innovation services.
Innovation management (Level Five)
Manages the provision and operation of data science innovation services.
Vendor engagement (Level Five)
Engages with and maintains vendor relationships.
Vendor management (Level Five)
Establishes vendor agreements/contracts and manages completion and disengagement.
Data transformation (Data Science) (Level Five)
Coordinates work that constructs data pipelines and datasets for analysis which draws on data engineering best practices.
Data exploration (Level Five)
Builds capability to extract maximum analytical value from the broadest range of data – structured and unstructured, internal and open – including exploiting potential to link and match data.
Data warehousing (Data Science) (Level Five)
Leads the selection and development of data engineering methods, tools and techniques. Ensures adherence to technical strategies and architectures.
Existing data sources (Level Five)
Ensures benefits of data acquisition, data modelling and data engineering are shared to benefit the wider analytical community.
Analytical potential (Level Five)
Enables the wider analytical group to derive maximum value from such systems through reuse and sharing of best practice.
Data integrity (Level Five)
Assesses issues which might prevent the organisation from making maximum analytical use of its information assets and recommends appropriate remedies.
Data linking (Level Five)
Drives the overall data management plan which supports data exploitation for analysis, including linking and matching across diverse datasets.
(Level Five)
Quality Assurance (Data Science) (Level Five)
Leads, develops and is accountable for an organisational approach and commitment to quality assurance and ethics. Ensures that quality assurance processes and ethics consideration activities are robust and based on industry best practice.
Legal and ethical (Level Five)
Considers the implications of emerging technology, approaches, trends, regulations and legislation.
Quality assessments DSC2.7 (Level Five)
Plans and resources the organisational quality assurance and ethics activities.
Non-compliance DSC2.8 (Level Five)
Reviews and analyses results from audit activities and identifies improvement opportunities for the organisation. Exercises sound ethical judgement in leadership of analytical work.
Control (DSC2.9) (Level Five)
Develops the organisation’s approach to embedding ethical & legal considerations within analytical work, including evidencing how such considerations are taken into account.
Reporting (DSC3.1) (Level Five)
Reviews findings and recommendations of analytical work from a wide range of sources with decision-makers and is able to convince senior officials about the implications of analytical evidence.
Key messages (DSC3.2) (Level Five)
Communicates key messages from analytical work in clear and concise lay terms for senior officials.
Explanation and recommendation (DSC3.3) (Level Five)
Influences external partners, stakeholders and customers successfully securing mutually beneficial outcomes.
Tailored presentation (DSC3.4) (Level Five)
Delivers confident and engaging presentations on data science and analytical work to a wide range of internal and external audiences.
Data visualisation (Data Science) (DSC3.5) (Level Five)
Demonstrates and promotes communicating data science with honesty, integrity, impartiality and objectivity; challenging others when communication of analysis does not meet these standards.
Improving outputs (DSC3.6) (Level Five)
Ensures analysis and data products developed by the organisation add distinct value amongst the wider ecosystem. Engages with senior peers in partner organisations to develop complementary strategies and focus delivery to maximise overall impact.
Tool selection (DSC3.7) (Level Five)
Influences and champions the use of presentation and dissemination tools at the organisation level or wider.
User needs (DSC3.8) (Level Five)
User research/design (DSC3.9) (Level Five)
Encourages the design and evaluation of data products through user engagement, establishing a culture of meeting diverse user needs through user centred design and continuous improvement.
Reporting processes (Level Five)
Promotes best practice in communicating analysis, including engaging with external debate.
Standards (Data Science) (Level Five)
Sets organisational standards for development and use of data science solutions.
Benefits and value (Level Five)
Partners with others to ensure tools and infrastructure for development and delivery of data products are fit for current and future purpose.
Analytics policy (Level Five)
Influences decision-makers to ensure capabilities and processes designed and delivered by others provide the infrastructure and environment that enable effective delivery of data science.
System context (Level Five)
Uses personal influence to make a positive difference across the NHS and externally.
Skill (Level Five)
5
Analytics techniques (DSC1.1) (Level Five)
Leads the provision of the organisations data science and analytics capabilities.
Analytics standards and policies (DSC1.2) (Level Five)
Technique application (DSC1.3) (Level Five)
Utilises awareness of leading-edge developments in analytics techniques and tools to identify and evaluate new opportunities for the organisation and leads on developing capability to take advantage of these.
Generating value (DSC1.14) (Level Five)
Directs the creation and review of a cross-functional, enterprise-wide approach and culture for generating value from data science and analytics.
Data science outputs (DSC1.4) (Level Five)
Ensures that the strategic application of data science and analytics is embedded in the governance and leadership of the organisation.
Evaluating data science techniques (DSC1.5) (Level Five)
Data science deployment DSC1.6 (Level Five)
Builds and leads data science capability to take advantage of a wide range of techniques enabling the efficient and effective analysis of structured and unstructured information which delivers business value.
Healthcare specific analytics DSC1.7 (Level Five)
Influences within and beyond own organisation to encourage adopting data science methods to add value for healthcare.
Analysis and reporting DSC1.8 (Level Five)
Communication of results DSC1.9 (Level Five)
Convinces senior officials of the value and the limitations of analytics.
Improvement of techniques DSC1.10 (Level Five)
Policy development (Level Five)
Expert advice (Level Five)
Technical architectures DSC1.13 (Level Five)
Promoting data science DSC2.1 (Level Five)
Oversees departmental data science resourcing- ensuring direct recruitment / retention of skilled data scientists and making effective use of external staff.
Professional development (Data Science) (DSC2.2) (Level Five)
Drives progressive professional and technical development of all staff and sets direction for data science professionals across the business area.
Professional networking (Data Science) (DSC2.3) (Level Five)
Identifies areas where data science can make fresh contributions.
Risk and reputation (Level Five)
Promotes the group’s reputation for professionalism, good service, and advanced analysis.
Analytics techniques (DSC1.1) (Level Five)
Leads the provision of the organisation’s data science and analytics capabilities.
Data requirements (Data Science) (Level Three)
Investigates data requirements where there is some complexity and ambiguity.
Data sources (Level Three)
Enables efficiencies by ensuring that information about data sources is documented and made available for the benefit of others.
Data quality (Data Science) (Level Three)
Carries out complex data quality checking and remediation.
Data structures (Level Three)
Provides advice and guidance to others using data structures.
Data standards (Level Three)
Provides advice on the transformation of data from one format or medium to another.
Accessibility (Level Three)
Takes responsibility for the accessibility, retrievability, quality, and ethical handling of data within analytical products.
Programming (Data Science) (Level Three)
Plans, designs, creates, amends, refactors, verifies, tests and documents complex programs/scripts and integration software services.
Development approaches (Level Three)
Contributes to selection of the development approach and tools for projects.
Engineering standards (Level Three)
Applies agreed standards to achieve well-engineered outcomes.
Development reviews (Level Three)
Participates in reviews of own work and leads reviews of colleagues’ work.
Development standards (Data Science) (Level Three)
Anticipates requirements for scalability, resilience, reproducibility and security and ensures these are addressed.
Automation (Level Three)
Operationalises and automates activities for the efficient and timely production of data products eg reproducable analytical pipeline (rap).
Dependencies (Level Three)
Clearly articulates dependencies on other teams / systems / infrastructure and builds these in to plans.
Academic/Industry links (Level Three)
Opportunities (Level Three)
Highlights opportunities offered by new data and technology.
Evolving technology (Level Three)
Keeps abreast of new and evolving technologies, tools, and analytical techniques, such as free and/or open source software, and suggests appropriate methods and techniques to incorporate in project work.
Emerging technology (Level Three)
Experimentation (Level Three)
Experiments with innovations, manages and learns from failures and shares lessons learned within and across teams.
Emerging technology selection (Level Three)
Works collaboratively with customers to develop proposals for the development of new data products that will meet user needs, including novel solutions that will allow customers to work in new ways and/or take advantage of previously untapped data resources.
Adoption (Data Science) (Level Three)
Alternative solutions (Level Three)
Interrogates datasets in novel ways to generate insight and/or add value to data products.
Proof of concept (Level Three)
Innovation strategy (Level Three)
Innovation management (Level Three)
Vendor engagement (Level Three)
Vendor management (Level Three)
Data transformation (Data Science) (Level Three)
Applies data analysis, design, modelling, and quality assurance techniques, to establish, modify or maintain data pipelines, datasets and metadata for diverse data types.