Data Security
Data Security is the ability to implement strong safeguards and systems to protect data. Involving expertise in encryption, access control and system resilience, it's utilised to keep information secure against breaches and attacks. Effective data security practices, therefore, ensure integrity, protect privacy and uphold the regulatory compliance of an organization's data assets.
Level 1: Emerging
At a foundational level you are aware of basic data security risks and follow established protocols to help keep data safe in engineering projects. You apply standard access controls and handle sensitive information as instructed by your team. Your actions help protect the organization’s data and support compliance with security requirements.
Level 2: Proficient
At a developing level you are following established data security practices in your engineering work, such as using basic encryption and access controls under guidance. You recognize potential vulnerabilities in data systems and raise concerns to more experienced colleagues. Your careful approach helps to reduce risks and maintain the integrity of organizational data.
Level 3: Advanced
At a proficient level you are able to apply established data security techniques, such as encryption and role-based access control, while building or managing data systems. You confidently identify security risks and implement controls that align with organizational and regulatory needs. Your efforts strengthen data privacy, reduce vulnerabilities and support ongoing compliance in complex data environments.
Structured and Unstructured Data Handling
Structured and Unstructured Data Handling' is the ability to process, categorise, and manage various types of data in an efficient way. It involves understanding different data architectures and systems, and using cutting-edge tools and methodologies for optimizing data flow and quality. The impact includes improved decision making, optimized business processes, and innovation in product or service offerings.
Level 1: Emerging
At a foundational level you are able to recognize the differences between structured and unstructured data, and can follow clear instructions to collect, store, or retrieve basic datasets. You work under guidance to support routine data tasks, contributing to consistent and reliable data handling. Your work helps others access the information they need for simple business activities.
Level 2: Proficient
At a developing level you are able to work with both structured and unstructured data using standard tools and simple techniques, following established processes and guidance. You can organize and prepare data sets for analysis or reporting, spotting basic quality issues as you go. Your contribution helps your team maintain reliable data sources, supporting accurate decisions and business improvements.
Level 3: Advanced
At a proficient level you are able to confidently process, organize, and manage both structured and unstructured data using modern data engineering tools and techniques. You apply sound practices to handle diverse data sources, ensuring data quality and seamless integration across systems. Your work leads to reliable data pipelines that support effective analysis and improve business outcomes.
Scalable Data Platform Design
Scalable Data Platform Design is the proficiency in building and developing data architectures that are flexible and optimisable for increased workload. It requires knowledge of the latest data storage technologies and high-level programming skills. This capability shows impactful in managing vast data flow efficiently, preventing system overloads, thus ensuring unobstructed data-driven processes.
Level 1: Emerging
At a foundational level you are aware of what makes a data platform flexible and able to handle more data as demand grows. You understand the basics of scalable data storage and can follow standard practices under guidance. This helps ensure your work supports reliable and efficient data flows in the team.
Level 2: Proficient
At a developing level you are beginning to design simple data platforms that can handle growing data needs within your team. You apply basic knowledge of storage options and programming to build solutions that support increased data flow without major issues. Your work helps to keep data accessible and processes moving smoothly as demands rise.
Level 3: Advanced
At a proficient level you are able to design and implement scalable data platforms that can handle growing data volumes without disruption. You choose appropriate storage solutions and optimize data flows to ensure reliability and efficiency across systems. Your work directly supports smooth data operations and helps teams make timely, informed decisions.
optimization of Data Workflows
optimization of Data Workflows is the ability to enhance the efficiency and effectiveness of data processing techniques. It involves amending existing data systems to reduce redundancies, accelerate throughput, and improve both quality and usability. A key impact is the accelerated decision-making process, driven by access to reliable and timely data insights.
Level 1: Emerging
At a foundational level you are able to follow established data workflows and recognize basic inefficiencies or delays in data processing tasks. You contribute to small improvements by suggesting simple changes or flagging issues to more experienced team members. Your efforts help maintain reliable and timely data flows, supporting smooth day-to-day operations.
Level 2: Proficient
At a developing level you are able to identify basic inefficiencies in data workflows and suggest straightforward improvements. You apply standard optimization methods under guidance, focusing on established systems and well-known bottlenecks. Your efforts help reduce obvious delays in data processing and slightly improve the reliability of reporting for your team.
Level 3: Advanced
At a proficient level you are able to independently analyze and refine data workflows to improve speed, reliability, and quality. You review existing processes, identify bottlenecks or inefficiencies, and implement practical enhancements using established data engineering tools and methods. Your work ensures data is available faster and supports better, more timely business decisions.
Monitoring and Logging (Data Systems)
Monitoring and Logging (Data Systems) is the ability to track, analyze and record data operations within complex systems. This relates to the keen understanding of data system architecture, alongside possessing the skills to interpret, troubleshoot and react to changes or inconsistencies in data logs. This capability significantly impacts system performance, reliability and integrity, boosting data-driven decision-making and business operations.
Level 1: Emerging
At a foundational level you are able to follow established processes to monitor and log activity within data systems, identifying basic errors or irregularities as they appear. You use standard tools to review logs and escalate issues to senior team members as needed. Your approach helps keep data systems running smoothly and supports the work of the wider data engineering team.
Level 2: Proficient
At a developing level you are able to use basic monitoring and logging tools to track data flows in established systems, reporting issues or irregularities as they arise. You’re starting to interpret simple log entries and follow set procedures to escalate or resolve common problems. This helps maintain reliable system operations and supports more experienced team members.
Level 3: Advanced
At a proficient level you are able to set up, maintain and interpret monitoring and logging tools across data pipelines and platforms. You identify, investigate and resolve operational issues independently, using logs to improve system reliability and performance. Your work ensures data flows remain stable and helps the team respond quickly to incidents.
Metadata Management
Metadata Management is the exercise of creating, controlling, enhancing, and preserving data definitions within a system. In a data engineering context, it involves understanding how to catalog, organize, locate and retrieve data efficiently. This capability enables the creation of high-quality, reliable data systems, impacting decision making and organizational performance.
Level 1: Emerging
At a foundational level you are able to identify and record basic metadata for data assets, following established templates and guidance. You understand why accurate metadata matters and can update data catalogs as directed, helping others to find and use data more easily. Your work supports the reliability and clarity of the organization’s data systems.
Level 2: Proficient
At a developing level you are starting to use metadata tools and processes to catalog and organize data sets within your projects. You follow established guidelines to help others locate and understand data more easily, supporting consistent data use across your team. Your work helps improve data quality and reliability for everyday tasks.
Level 3: Advanced
At a proficient level you are able to manage and maintain metadata catalogs within data engineering systems, ensuring data assets are consistently described, accessible, and reliable. You use established processes to organize and upgrade metadata, making it easier for teams to locate and use data effectively. Your approach helps improve data quality and supports confident, informed decision making.
ETL / ELT Pipeline Development
ETL / ELT Pipeline Development is the ability to competently design, implement, and manage Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) data pipelines. Positively impacting efficient data processing, the capability involves understanding data sources, applying transformation logic, and delivering to target databases. Ultimately, it carries a significant influence on reliable decision-making and insights generation from vast data sets.
Level 1: Emerging
At a foundational level you are able to follow established guidance to build and run basic ETL or ELT data pipelines with support. You understand the key steps involved, such as extracting data, applying simple transformations, and loading it into target systems. Your work helps others to access accurate and timely data, supporting day-to-day operations.
Level 2: Proficient
At a developing level you are able to build and maintain basic ETL or ELT pipelines with guidance from more experienced team members. You use standard tools to connect to common data sources, apply straightforward transformation steps, and load data into target systems. Your work supports small projects and helps improve data reliability for routine tasks.
Level 3: Advanced
At a proficient level you are able to independently design, build, and maintain robust ETL and ELT pipelines to support business requirements. You confidently handle complex data transformations and tune pipeline performance, ensuring data is accurate, up-to-date, and delivered reliably to key systems. Your work improves the quality and speed of downstream data-driven decisions.
Database Design
Database Design is the multidimensional process of creating and managing data structures to meet specific business needs in data engineering. It involves the systematic creation, testing, and refinement of complex databases. This capability facilitates strategic decision-making, optimizes data integration and improves operational efficiency.
Level 1: Emerging
At a foundational level you are learning the basics of database design within data engineering, such as understanding tables, relationships, and simple data models. You can follow established instructions to help set up or modify small databases under guidance. Your work supports accurate data organization and lays the groundwork for more advanced database tasks.
Level 2: Proficient
At a developing level you are able to contribute to designing and updating simple database structures under guidance from experienced colleagues. You apply basic principles of data modeling and help test designs against business requirements. Your work supports better integration and reliability of data used in engineering projects.
Level 3: Advanced
At a proficient level you are able to design, build, and refine relational and non-relational databases that support complex data engineering solutions. You consider business requirements, data quality and integration in your work, ensuring databases are optimized for performance and reliability. Your designs actively enable smoother data flows and drive better decision-making across the organization.
Data Warehousing
Data Warehousing is the organization, storage, and management of large datasets for the purpose of reporting and analysis. By enabling the integration and transformation of disparate data into a unified view, it facilitates informed decision-making. Mastery of Data Warehousing requires an in-depth comprehension of data structures, querying languages, and data modeling, along with a keen ability to troubleshoot and optimize for improved system performance.
Level 1: Emerging
At a foundational level you are familiar with the basic concepts and purpose of data warehousing in supporting data engineering work. You can identify what data warehouses do, follow set processes for loading and retrieving data, and understand how they help organize and store large datasets. Your involvement enables efficient participation in routine data management tasks.
Level 2: Proficient
At a developing level you are able to support data warehouse tasks by following established processes and using basic querying tools under supervision. You can help organize and load data, check data quality, and create simple reports. Your work helps the team keep information organized and accurate for analysis.
Level 3: Advanced
At a proficient level you are able to design, build, and maintain data warehouses that support reliable business reporting and analytics. You integrate data from multiple sources, apply effective data models, and optimize queries to improve efficiency. Your work ensures stakeholders have timely access to accurate and well-structured information for decision-making.
Data Quality Management
Data Quality Management is the proficient practice of validating, enhancing and maintaining the quality of data. In a data engineering context, this involves building appropriate systems to cleanse raw data and monitor its ongoing quality. High-quality, accurately processed data significantly impact decision making, enhancing the validity of insights yielded.
Level 1: Emerging
At a foundational level you are learning how to identify basic data quality issues and follow clear steps to help cleanse and validate data as part of simple data engineering tasks. You work under guidance to support data quality checks and understand their importance in maintaining reliable datasets. Your actions help others use more accurate and trustworthy data.
Level 2: Proficient
At a developing level you are starting to identify data quality issues and follow established processes to address them within data pipelines. You support routine data cleansing and validation tasks, guided by more experienced team members. Your actions help improve the reliability of data, supporting better business decisions over time.
Level 3: Advanced
At a proficient level you are able to design and implement robust processes that check, clean, and enrich data as it moves through engineering pipelines. You monitor data quality with automated checks and act quickly to resolve quality issues. Your work ensures reliable, accurate datasets that support trusted business reporting and analytics.
Batch and Stream Processing
Batch and Stream Processing is a key capability in data engineering which involves managing and analyzing large and continuous data flows. It prioritizes the ability to process high volume datasets in batches or execute real-time analysis through streaming. This capability drives informed business decisions, enabling prompt action on insights derived from data.
Level 1: Emerging
At a foundational level you are familiar with the basic concepts of batch and stream processing in data engineering. You understand the difference between processing large volumes of data in groups versus handling continuous data in real time. You can follow established processes and support simple data tasks that contribute to timely and accurate business insights.
Level 2: Proficient
At a developing level you are able to support the setup and operation of batch or stream processing tasks using established tools under guidance. You can follow defined procedures to process and monitor data flows, spotting basic issues and raising them when needed. Your contribution helps your team deliver reliable data for analysis and reporting.
Level 3: Advanced
At a proficient level you are able to design, build, and optimize batch and stream processing pipelines that handle high-volume data for both scheduled and real-time analysis. You choose the appropriate processing methods for different business needs and troubleshoot issues as they arise. Your work ensures data flows reliably, enabling teams to make timely, evidence-based decisions.
Data Pipeline Orchestration
Data Pipeline Orchestration is the capability of managing and automating the flow of data from multiple sources to a destination efficiently. This involves monitoring the process, identifying bottlenecks, and implementing data storage solutions. The outcome improves an organization's decision-making abilities through seamless, accurate and timely data delivery.
Level 1: Emerging
At a foundational level you are able to support basic data pipeline tasks by following clear instructions and using standard tools provided by your team. You recognize when data is flowing as expected and can report simple issues to others. Your actions help ensure data moves reliably to where it is needed for business decisions.
Level 2: Proficient
At a developing level, you assist with building and running basic data pipelines under guidance, helping to move data from source to destination. You follow established processes, start to notice issues such as failed jobs or slow transfers, and share your observations with others. Your work supports timely and accurate data delivery for decision-making.
Level 3: Advanced
At a proficient level you are able to design, build, and maintain automated data pipelines that reliably move data between systems. You identify and fix common issues, such as delays or data inconsistencies, and suggest improvements to optimize data flow. Your work ensures that accurate, timely data is delivered to support better business decisions.
Data modeling
Data modeling is a critical facet of Data Engineering. It involves conceptualising and organizing data structures to build robust, efficient databases. Performed well, this enhances data consistency, reduces redundancy and improves data integrity, ultimately supporting astute decision-making.
Level 1: Emerging
At a foundational level you are able to recognize basic data structures and understand how data is organized within simple databases. You contribute to tasks like documenting data fields and following established data modeling guidelines under supervision. Your work helps support reliable and consistent data that others can use with confidence.
Level 2: Proficient
At a developing level you are able to create basic data models under guidance, applying standard principles to organize and structure data. You work with existing schemas and make simple changes to support project needs. By doing this, you help ensure data is consistent and reliable for others in your team.
Level 3: Advanced
At a proficient level you are able to design and implement data models that are fit for purpose and scalable across a range of data engineering projects. You use sound modeling techniques to organize data efficiently, ensuring reliability and consistency. Your work enables teams to access quality data, supporting better business decisions and smoother operations.
Data Lake Design
Data Lake Design is the ability to create efficient, scalable structures for storing vast amounts of raw data. Ideal for a data engineer, it involves understanding and defining data origin, format and relevance to ensure it's readily available for use. Its effective implementation provides robust data access, enhancing analysis and resulting in better-informed business decisions.
Level 1: Emerging
At a foundational level you are aware of what a data lake is and why it matters, recognizing its role in storing large amounts of raw data for future use. You can identify data sources and basic formats, and support simple storage tasks under guidance. Your actions help ensure reliable access to data for more advanced engineering work.
Level 2: Proficient
At a developing level you are able to contribute to the design of data lake structures by following established guidelines and best practices. You help organize raw data, ensuring key details like source, format, and relevance are recorded accurately. Your involvement supports efficient data access for your team, making future analysis more straightforward.
Level 3: Advanced
At a proficient level you are able to design and implement data lakes that efficiently store and organize large volumes of varied raw data. You clearly define data sources, formats, and access needs, ensuring data is both secure and easy to retrieve for analysis. Your work supports reliable insights and improves decision-making across the business.
Data Integration
Data Integration is the capability combining data from different sources to provide unified, usable information. Central to data engineering, it enables effective analytics and insights. Mastery improves decision-making, optimizes operations, and fuels innovation.
Level 1: Emerging
At a foundational level you are able to identify and collect data from basic sources following set instructions. You use simple tools and techniques to combine data, with guidance, to support routine data engineering tasks. Your work helps lay the groundwork for more advanced data integration and ensures accurate input for the team’s analysis.
Level 2: Proficient
At a developing level you are able to support data integration tasks by following established processes and applying basic data engineering techniques. You combine data from a limited range of sources under supervision, paying attention to consistency and quality. Your work helps ensure that data can be used for simple reporting and analysis across teams.
Level 3: Advanced
At a proficient level you are able to reliably combine data from multiple sources using established tools and processes, ensuring the data you deliver is accurate, complete, and ready for analysis. You work independently on integration tasks across projects, spotting and resolving typical issues. Your work enables teams to access unified data, supporting better business insights and outcomes.
Data Infrastructure Automation
Data Infrastructure Automation is the capability and knowledge to automate the management, scaling and securing of data infrastructures. Covered within the ambit of data engineering, it saves time and reduces error risks. This capability strengthens data reliability and expedites engineering tasks, driving overall project efficiency.
Level 1: Emerging
At a foundational level you are learning to use basic tools to help automate simple data infrastructure tasks, such as setting up or updating databases. You follow established instructions and seek guidance to ensure your work is reliable and secure. Your efforts help your team save time and avoid mistakes in routine data engineering activities.
Level 2: Proficient
At a developing level you are beginning to automate basic data infrastructure tasks under guidance, such as provisioning servers or scheduling simple backup routines. You follow existing processes and use standard tools to reduce manual effort and errors. Your work supports more efficient data engineering operations and helps ensure reliable data management.
Level 3: Advanced
At a proficient level you are able to design, implement, and maintain automated solutions that manage and scale data infrastructure with minimal oversight. You consistently use automation tools and scripts to ensure reliability, efficiency, and security, supporting seamless data engineering workflows. Your efforts reduce manual errors and improve operational speed across data projects.
Data Governance Implementation
Data Governance Implementation is the practical application of data governance policies in a data engineering context. It involves understanding the ethical, legal, and operational considerations associated with handling data, and translating these into effective procedures. This capability is crucial, directly impacting an organization's ability to use data responsibly, strategically and legally.
Level 1: Emerging
At a foundational level you are aware of your responsibilities in handling data and follow basic data governance guidelines set by your team or organization. You seek guidance when faced with ethical, legal, or operational questions, ensuring you apply data policies correctly in your daily engineering tasks. By doing so, you help build trust in your team’s use of data.
Level 2: Proficient
At a developing level you are starting to put data governance policies into action within your data engineering work, following established guidelines and seeking direction when needed. You recognize the importance of handling data ethically and legally, and begin to adjust your processes to meet these standards. Your efforts help build a safer, more compliant approach to data management in your team.
Level 3: Advanced
At a proficient level you are able to implement data governance procedures within data engineering projects, ensuring that data handling aligns with relevant policies and regulations. You consider ethical, legal, and operational factors when establishing and maintaining controls. Your work helps the organization use data responsibly, keeping it safe and compliant while supporting business needs.
Data Architecture Implementation
Data Architecture Implementation is the application of ability in establishing and following a planned data architecture within a data engineering environment. This capability extends into managing data structure designs, data flowcharts, and other complex data mechanisms. Its efficacy is marked by optimizing systems for better data handling, thereby engendering improved business decision-making and outcome forecasting.
Level 1: Emerging
At a foundational level you are learning to follow established data architecture guidelines and document simple data structures within a data engineering setting. You contribute by supporting the implementation of basic data flows under close guidance, ensuring information is organized clearly for team use. Your efforts help lay the groundwork for reliable data management and accurate business reporting.
Level 2: Proficient
At a developing level you are beginning to apply data architecture principles within data engineering projects, supporting the creation and maintenance of data models and flow diagrams under guidance. You work on assigned tasks to help implement planned data structures, learning how your work supports better data management and business insights. Your contributions assist the team in improving system reliability and data quality.
Level 3: Advanced
At a proficient level you are able to implement planned data architectures with consistency and accuracy in complex data engineering environments. You design and maintain effective data structures and flows, ensuring systems are optimized for efficiency and reliability. Your work improves the quality of data available, supporting clearer business decisions and better forecasting.
