NCFE Level 5 Diploma: Data Engineer

What you'll be able to do

• Explore and analyse the inherent risks of data collection and usage Knowledge: Risks associated with data collection: • privacy and security risks • non-compliance issues • ethical concerns • misuse of data • data quality issues: o incomplete data o inaccurate data o inconsistent data. The potential impact of poor data quality on organisations.
• Explore frameworks for data quality and how contributing dimensions support reliability Knowledge: Components of data quality frameworks: • governance • profiling • quality dimensions: o accuracy o completeness o consistency o timeliness o accessibility o uniqueness • cleansing • validation • continuous monitoring and reporting: o validity. Features of common data quality frameworks (for example, DAMA UK, TDQM). How to resolve identified risks to ensure data quality and to mitigate against future issues. Skills: Monitor and analyse data quality metrics to meet operational requirements when processing data, considering: • quality • accuracy • reliability.
• Develop a data product that complies with regulatory requirements Knowledge: Key considerations when developing a data product: • system design • compliance and metrics (for example, regulatory requirements) • resource requirements • scalability. Skills: Apply data product design methods to meet requirements: • collation of information and requirements • creating an entity relationship diagram (ERD) • testing and evaluation • refinements: o user requirements o business requirements: ▪ cost ▪ resourcing ▪ accessibility.
• Explore the principles of data warehousing Knowledge: The features of the ETL (extract, transform, load) process, and their role in preparing data for analysis. How to use an ETL pipeline to connect disparate data sources to meet business requirements. Core principles of data warehousing: • Kimball’s dimensional data modelling • Inmon’s data warehouse model. The principles for design and implementation: • ETL process • schema design (for example, star, snowflake) • separation of operational and analytical systems • data quality and consistency • performance and scalability • security and access control. The role of data lakes, data warehouses and data marts in meeting business requirements. Skills: Manage data manipulation tasks, applying appropriate tools and techniques (for example, filtering, joining sorting), ensuring data integrity.
• Analyse relevant policies and strategies to ensure business continuity when implementing data solutions Knowledge: Features of key business continuity strategies (for example, system monitoring and surveillance), and their role in ensuring organisational resilience and the recovery of important data and services. The role of policies to support business continuity and mitigate against negative impact in relation to data provision: • business continuity • data backup • disaster recovery • incident reporting. Skills: Apply shell script (for example, Bash) to monitor availability, log downtime, and communicate findings to stakeholders through automated messages.

What you'll be able to do

• Explore the principles of data Knowledge: Principles of data: • lawfulness, fairness, and transparency • purpose limitation • data minimisation • accuracy • storage limitation • integrity and confidentiality • accountability. Types of datasets: • open and public • administrative • research. Approaches to gaining data through research. The value of external data sources for enriching internal data. How direct data acquisition can enhance business operations.
• Explore the concepts of data governance Knowledge: Appropriate legislation and how it is applied to the safe use of data: • UK General Data Protection Regulation (GDPR)/Data Protection Act 2018 • Digital Economy Act 2017 • Computer Misuse Act 1990. Considerations for regulatory requirements: • data and information security standards: o NIST o PCI DSS. The use of data governance frameworks (for example, government led (UK), industry recognised). Features of policies and procedures relating to data governance: • compliance with regulatory requirements • data privacy • data anonymisation • data ownership • data lineage • ethical usage • metadata • security • quality control: o completeness o timeliness o consistency. Skills: Apply data solutions to address common organisational requirements. Work collaboratively with appropriate stakeholders to meet requirements.
• Explore and apply the principles of data normalisation and redundancy in relational databases Knowledge: Principles of data normalisation: • Codd’s 12 rules • normal form (NF): o 1NF – eliminating duplicate records o 2NF – eliminating partial dependencies o 3NF – eliminating transitive dependencies o BCNF (Boyce-Codd normal form) – a stricter version of 3NF o 4NF – removing multi-valued dependencies o 5NF – eliminating join dependencies. Features of relational database management systems (RDBMSs). Advantages of data normalisation to databases with regards to key concepts: • data protection • data redundancy • inconsistent dependency. Skills: Query, manipulate and organise data to meet requirements, using industry- standard tools and programming languages (for example, SQL and Python). Manage and implement database access controls to meet business requirements: • access levels (for example, resource, administrator) • types of access (for example, guest, standard) • access control methods (for example, MAC, RBAC). Implement automated validation checks.
• Explore approaches to data integration Knowledge: Approaches to data integration: • manual integration • API (application programming interface) integration • third-party integrations • direct integration. How integration of disparate data sources supports business outcomes.
• Explore the unique features and functions of different data formats Knowledge: Characteristics of different data formats. Data formats: • structured • semi-structured • unstructured. How different data formats can be stored: • data stores: o relational database (SQL) o non-relational database (NoSQL) • distributed systems: o distributed file systems (for example, Apache Hadoop, Amazon S3) o distributed database o distributed NoSQL o distributed object storage. Skills: Apply a range of data storage solutions and distributed file systems to implement a system that meets specified requirements. Use data systems securely to meet requirements, in compliance with relevant legislation, policies and procedures.
• Explore common data development frameworks and architectures Knowledge: Processes involved in developing the data product to function within the organisation. Characteristics of common architectures: • cloud • on-premises • monolithic • modular. Features and uses of common frameworks (for example, Apache Spark, DBT, Apache Airflow).

What you'll be able to do

• Explore approaches used for handling and storing data Knowledge: The considerations and use of data handling and storage approaches. Considerations: • relational and non-relational storage • cloud and on-premises solutions • structured versus unstructured data handling. Handling approaches: • ETL (extract, transform, load) • ELT (extract, load, transform) • API (application programming interface)-based transfer • manual transfer. Storage approaches: • data warehousing • data lakes • cloud storage and backups • archive and retention. Processes and considerations involved in supporting the movement of data during cloud migration: • migration planning • data transfer methods • tools and platforms • security and compliance • testing. The considerations of different data transfer methods (for example, batch, streaming, on-demand) to meet business requirements.
• Explore the application of data transfer from one location to another Knowledge: Features and use cases of data ingestion frameworks (for example, Apache NiFi). Different data ingestion modes (batch, streaming, and on-demand) and their application for data processing. Appropriate tools and technologies used for streaming, batching, and on- demand data transfer between systems (for example, Apache Kafka, ETL platforms, REST APIs). The 7 Rs of cloud migration as a strategy for moving applications and data to the cloud. Types and implementation of migration strategies (for example, big bang, trickle, zero downtime) to meet requirements. Skills: Use different techniques to optimise data ingestion processes using appropriate data ingestion frameworks: • batch • streaming • on-demand.
• Explore deployment methods for data pipelines and automated processes Knowledge: Functions and features of pipeline deployment methods (for example, manual, scheduled, serverless) to ensure data flows reliably, securely, and efficiently. How version control and rollback planning are critical in data pipeline development and deployment: • collaboration • tracking • automated testing • recover from failure. Techniques and tools to effectively identify and troubleshoot issues within data processing pipelines: • monitoring and logging tools (for example, AWS CloudWatch) • debugging methodologies (for example, unit testing) • performance tuning techniques (for example, parallel processing). Skills: Apply appropriate techniques and tools to identify and troubleshoot issues with data processing pipelines.
• Explore the types and uses of common data engineering tools and applications Knowledge: Features and application of data engineering tools used to design, build, manage, and optimise data pipelines and infrastructure: • data ingestion tools (for example, Apache NiFi) • data transformation tools (for example, Talend) • data storage tools (for example, Snowflake) • orchestration and workflow management tools (for example, Prefect) • data monitoring (for example, Monte Carlo). Common programming languages used in data engineering: • Python • SQL • Scala • Java • Bash/Shell • R • Golang. The role of YAML as a configuration and metadata format in data engineering. How data engineering tools, programming languages, and configuration formats are applied within an organisation to design, build, automate, and manage data pipelines and infrastructure. Skills: Automate batch, real-time, and on-demand data pipelines utilising programming languages and data integration platforms with graphical user interfaces or infrastructure-as-code tools. Develop and apply appropriate processes and algorithms to support extraction of structured data from unstructured data.

What you'll be able to do

• Explore the principles of software development for data products Knowledge: The principles of software development and how to apply them to data products: • version control • designing for modularity, reusability, and maintainability • code readability and documentation • debugging, logging, and traceability • automated testing and CI/CD (continuous integration/continuous deployment) • privacy by design. The role of development (DEV), staging (STG), and production (PRD) environments, and how each supports the software development lifecycle. The use of best practice approaches (for example, DRY) to enhance code quality, reusability, and testability within data-centric environments. Skills: Produce and maintain technical documentation for the data product containing the required technical and non-technical information for stakeholders. Contribute to and apply best practice approaches in software development principles when working with stakeholders.
• Analyse how to cost and build a system to align with organisational sustainability responsibilities Knowledge: Considerations when costing a system build: • requirements analysis (for example, features) • materials (for example, hardware, software) • staffing and operational costs (for example, development) • cost estimation methods (for example, return on investment (ROI)) • cloud versus on-premises. Factors to consider when undertaking and implementing a system build: • architecture design (for example, modular) • technologies (for example, cloud services) • development environment (for example, CI/CD) • testing. How system design and implementation decisions can be aligned with organisational sustainability strategies (for example, E-waste reduction). Skills: Design a secure data product to meet business and user requirements.
• Explore the appropriate use of technology and service management in relation to data products Knowledge: How configuration management practices maintain accurate and up-to-date information on IT assets and systems to support effective service delivery and change control. The role and use of change management processes to control, assess, and authorise system modifications. Techniques to manage and respond to IT incidents by prioritising, diagnosing, and resolving service disruptions. How industry-standard tools (for example, service desk) support service management activities. How service management practices align with recognised frameworks and standards. Skills: Review use and configuration of existing tools and techniques to identify potential implementation issues to support business change or transitions.
• Explore the processes for evaluating and implementing prototypes within a production environment Knowledge: How to evaluate prototypes against defined criteria (for example, functionality, usability) to determine readiness for implementation. Developmental process to refine prototypes into production-ready solutions. How release strategies (for example, phased) are used to support the effective implementation of prototypes. Skills: Evaluate prototype data products by assessing their cost–benefit value, compatibility with organisational data infrastructure, and the availability of open-source tools and online support.
• Explore the lifecycle of implementing data solutions within a business Knowledge: Processes involved in the lifecycle of implementing data solutions within a business: • ensure that data solutions are scoped accurately and align with business objectives, with consideration for: o business context o stakeholder needs o success criteria • evaluation of prototype data solutions to test feasibility, validate technical approaches, and gather feedback to inform further development • the building and integration of data pipelines, models, and interfaces in line with design specifications • completion of testing and implementation of production deployment strategies • evaluation and monitoring of the performance of implemented data solutions. Skills: Review and monitor the health and performance of data systems (for example, identifying technical debt) as part of continuous improvement.