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Current WGU employees should submit an internal application before 5/17/2022 to be considered.
The Principal Data Engineer should be agnostic to tools and should be able to supervise, design, architect and code using Apache Spark and other cloud technologies. The position will supervise and design how data will flow through hybrid data environments comprised of open-source big data platforms and traditional database systems. The core responsibility for this position includes supervision of data engineering technical aspects, design of data and system architecture for the Data Lake and data warehouse, supervision of the technical aspects of a data engineering team and projects encompassing dimensional and normalized data modeling.
The Principal Data Engineer will design technical standards in the environment ensuring optimal use of data warehouse and other data stores to solve business problems. They will serve as the principal engineer for all aspects of the data engineering team including solution architecture research, data modeling, architecture of data systems and mentoring of engineers.
ESSENTIAL FUNCTIONS AND RESPONSIBILITIES
Supervision, design, and implementation of end-to-end data pipeline projects including Data Lake(s), Data Warehouse(s), and Data Marts.
Establish design and methodology for database build processes.
Supervise the architecture and design of complete data model solutions.
Supervise necessary data protection and security processes.
Translate business problems/information requirements accurately to logical/physical data models aligning with customers’ data architecture standards.
Supervise and perform research and analysis to find solutions for complex business problems.
Supervise the profiling of data, the publishing of data profiles and corrective actions if required to ensure data quality.
Supervise and perform documentation / reverse engineering / analysis of data mapping using data integration code/tools.
Work with APIs for data wrangling and integrations with other systems data in the EDW.
Perform impact analysis using Data Integration/Data Virtualization tool repositories, DB data dictionary, UNIX scripts and frontend code on versioning systems.
Analyze / research data on multiple platforms as wells as multiple heterogeneous databases including custom developed databases.
Operationalize machine learning models for production with continuous deployment and retraining capabilities.
Positively impact projects by ensuring team tasks are assigned, understood, and completed on time.
Communicate technical and domain knowledge as it relates to work, to both technical and non-technical audiences.
Ingest and transform structured, semi structured and unstructured data from sources including relational databases, NoSQL, external APIs, JSON, XML, delimited files, and more.
Support business and functional requirements and translate these requirements into robust, scalable, solutions.
Collaborate with engineers to help adopt best practices in data system architecture, data integrity, test design, analysis, validation, and documentation.
Help continually improve ongoing reporting and analysis processes, automate or simplify self-service modeling and production support for customers.
Higher Education domain exposure
Expertise with analytical reporting tools, preferably Cognos and Tableau
Mastery in code based ETL/ ELT tools for importing and exporting data across disparate systems
Expertise in analytic skills related to working with unstructured datasets
Use of industry best practices for code development, testing, implementation and documentation
Ability to multitask and prioritize work based on the organization needs
Ability to mentor Associate/Senior/ Staff/ Data Engineer in data pipeline architecture and coding standards
Ability to supervise cross team projects to accomplish data integrations and pipelines
Supervisory abilities for data engineering team with respect to technical design and architecture
Excellent verbal & written communication, along with technical documentation
Ability to work and deliver in a team environment
Deep understanding of data integration to support analytics & feature engineering for Machine learning algorithms
Ability to manage the use of tools like Jira, Confluence, GitHub
Ability to architect, develop, and communicate processes for audit of data integrity and security
Supervise validation and testing for pipeline debugging and data corrections
Ability to work with statistical tools/ languages like R/ SPSS/ SAS
Mastery of relational SQL and NoSQL databases
Mastery with object-oriented/object function scripting languages: Python, Java, Scala
Mastery of cloud data tools: AWS, Spark, Kafka, Databricks, Hadoop, etc.
Desired Technical Skills
Data Cataloging Collibra
AWS cloud technologies
REQUIRED EXPERIENCE AND/OR EDUCATION
M. S. / B. S. in Business, Management Information Systems, Computer Science, or a related field, or an equivalent combination of experience and training.
Nine or more years of experience as a Data Engineer, Data Integration, Big Data, or Business Intelligence, Software Engineer
As an equal opportunity employer, WGU recognizes that our strength lies in our people. We are committed to diversity.