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Engineering and Architecture Category National Occupational Standard Framework for Data & Artificial Intelligence KNOWLEDGE Knowledge of general principles, concepts and practices in Data Management and organization. Knowledge of data warehousing and data mining. Knowledge of main concepts in data processing (such as data cleaning, data validation, data verification and data transformation). Knowledge of data security and protection. Knowledge of infrastructure and platforms for data science applications. Knowledge of data Infrastructure: services and components, including data storage infrastructure. Knowledge of data processing models (batch, steaming, parallel) Knowledge of navigating solutions for complex data. Knowledge of big data solutions for large scale data processing. Knowledge of large and ultra-large scale software systems organization & warehouse platforms. Knowledge of metadata registries, publishing metadata, and systems integration. Knowledge of computer networking concepts and protocols, and network security methodologies. D-En-K01 D-En-K02 D-En-K03 D-En-K04 D-En-K05 D-En-K06 D-En-K07 D-En-K08 D-En-K09 D-En-K10 D-En-K11 D-En-K12 SKILLS Skill in designing, coding, testing, correcting & documenting simple-to-complex programs and scripts from agreed specifications and subsequent iterations. Skill in undertaking data profiling & sourcing system analysis. Skill in maintaining a repository to ensure information accuracy and quality. Skill in seting up robust governance and security processes to keep repositories up to date. Skill in knowing how to best to optimize data infrastructure. Skill in explaining the types of problems in databases, data processes, data products and services. Skill in extraction-transformation-loading (ETL) process. Skill in identifying technical solutions for complex data. Skill in designing, building and testing data products that are complex or large scale Skill in data integration methods and frameworks. Skill in producing synthetic data and use it for data analytics. D-En-S01 D-En-S02 D-En-S03 D-En-S04 D-En-S05 D-En-S06 D-En-S07 D-En-S08 D-En-S09 D-En-S10 D-En-S11 National Occupational Standard Framework for Data & Artificial Intelligence - A bachelor's degree in computer science, statistics, software engineering information technology or a related discipline is usually required. - A master's or doctoral degree in data science, machine learning, economics, science (if curricula contain data analysis) or a related quantitative field is usually preferred. EDUCATION EDUCATION - Experience in data engineering techniques and methodologie is usually required. EXPERIENCE EXPERIENCE ABILITIES ABILITIES Ability to deal with complex technical problem. Ability to work with one of the well-known big data analytics platforms and tools (such as Hadoop, Spark, and cloud based big data services). Ability to build complex data structures and high-level programming languages. Ability to dissect a problem and examine the interrelationships between data that may appear unrelated. Ability to use and understand complex mathematical concepts (e.g., discrete math). Ability to multi-task, collaborate with peers, customers, and management of oneself or others to accomplish a variety of different tasks in a constantly changing environment. D-En-A01 D-En-A02 D-En-A03 D-En-A04 D-En-A05 D-En-A06 National Occupational Standard Framework for Data & Artificial Intelligence