AddressData Engineer
What you'll do at Position Summary.
What you'll do... Tech. Problem Formulation Requires knowledge of Analytics/big data analytics/automation techniques and methods; Business understanding; Precedence and use cases; Business requirements and insights. To identify possible options to address the business problems within one's discipline through relevant analytical methodologies. Demonstrate understanding of use cases and desired outcomes.
Understanding Business Context Requires knowledge of Industry and environmental factors; Common business vernacular; Business practices across two or more domains such as product, finance, marketing, sales, technology, business systems, and human resources and in-depth knowledge of related practices; Directly relevant business metrics and business areas. To support the development of business cases and recommendations. Drives delivery of project activity and tasks assigned by others. Supports process updates and changes. Support, under guidance, in solving business issues.
Data Governance Requires knowledge of Data value chains; Data processes and practices; Regulatory and ethical requirements around data; Data modeling, storage, integration, and warehousing; Data value chains (identification, ingestion, processing, storage, analysis, and utilization); Data quality framework and metrics; Regulatory and ethical requirements around data privacy, security, storage, retention, and documentation; Business implications on data usage; Data Strategy; Enterprise regulatory and ethical policies and strategies. To support the documentation of data governance processes. Supports the implementation of data governance practices.
Data Strategy Requires knowledge of Understanding of business value and relevance of data and data-enabled insights/decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards, Data Governance, Accessibility, Storage and Scalability, etc; Understanding of the methods and applications that unlock the monetary value of data assets. To understand, articulate, and apply principles of the defined strategy to routine business problems that involve a single function.
Data Source Identification Requires knowledge of Functional business domain And scenarios; Categories of data and where it is held; Business data requirements; Database technologies and distributed datastores (e.g. SQL, NoSQL); Data Quality; Existing business systems and processes, including the key drivers and measures of success. To support the understanding of the priority order of requirements and service level agreements. Helps identify the most suitable source for data that is fit for the purpose. Performs initial data quality checks on extracted data.
Data Transformation and Integration Require knowledge of: Internal and external data sources including how they are collected, where and how they are stored, and interrelationships, both within And external to the organization; Techniques like ETL batch processing, streaming ingestion, scrapers, API and crawlers; Data warehousing service for structured and semi-structured data, or to MPP databases such as Snowflake, Microsoft Azure, Presto or Google BigQuery; Pre-processing techniques such as transformation, integration, normalization, feature extraction, to identify and apply appropriate methods; Techniques such as decision trees, advanced regression techniques such as LASSO methods, random forests, etc; Cloud and big data environments like EDO2 systems. To extract data from identified databases. Creates data pipelines and transforms data into a structure that is relevant to the problem by selecting appropriate techniques. Develops knowledge of current data science and analytics trends.
Data Modeling Requires knowledge of Cloud data strategy, data warehouse, data lake, and enterprise big data platforms; Data modeling techniques and tools (For example, Dimensional design and scalability), Entity-Relationship diagrams, Erwin, etc.; Query languages SQL / NoSQL; Data flows through the different systems; Tools supporting automated data loads; Artificial Intelligent - enabled metadata management tools and techniques. To analyze complex data elements, systems, data flows, dependencies, and relationships to contribute to conceptual, physical, and logical data models. Develops the Logical Data Model and Physical Data Models including data warehouse and data mart designs. Defines relational tables, primary and foreign keys, and stored procedures to create a data model structure. Evaluates existing data models and physical databases for variances and discrepancies. Develops efficient data flows. Analyzes data-related system integration challenges and propose appropriate solutions. Creates training documentation and trains end-users on data modeling. Oversees the tasks of less experienced programmers and stipulates system troubleshooting supports.
