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Regular or Temporary:

Regular

Language Fluency:  English (Required)

Work Shift:

1st shift (United States of America)

Please review the following job description:

The Digital Workplace Analytics Engineer - ETL & Business Insights will be an individual contributor role in the Teammate Support Experience function, analyzing how our teammates experience support, our products, automation, and self-service adoption.
This teammate will be familiar with Employee Technology products and services, and have technical expertise in SQL, ETL, Tableau, PowerBI, and other visualization tools. They will also be familiar with ServiceNow table structure, writing scripted API calls, SQL stored procedures, and basic database administration. This position will enable data-driven business decisions around product strategies, automation, and reducing contacts to the IT Service Desk.
Day-to-day will involve developing, delivering and maintaining datasets, dashboards, reports, and analytics products to support Digital Workplace, as well as ingesting data from multiple systems of record via API, flat file, SharePoint, and other means. Researching and resolving information flow and data content issues, and analysis of complex datasets will be required.

ESSENTIAL DUTIES AND RESPONSIBILITIES
Following is a summary of the essential functions for this job.  Other duties may be performed, both major and minor, which are not mentioned below.  Specific activities may change from time to time.  
1.    Independently perform sophisticated data analytics (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data.
2.    Produce compelling data visualizations to communicate insights and influence outcomes among a wide array of stakeholders.
3.    Take accountability and ownership of end-to-end data science solution design, technical delivery, and measurable business outcome.
4.    Engage in stakeholder meetings to identify business objectives and scope solution requirements.
5.    Independently write, document, and deploy custom code in a variety of environments (Python, SAS, R, etc.) to create predictive analytics applications. 
6.    Use, maintain, share and collaborate through Truist internal code repositories to foster continual learning and cross-pollination of skillsets. 
7.    Actively research and advocate adoption of emerging methods and technologies in the data science field, with the eye of continually advancing Truist’s capabilities. 
8.    Exercise sound judgment and foster risk management culture throughout design, development, and deployment practices; partner with cross-functional teams to coordinate rules on data usage, data governance and analytics capabilities.

QUALIFICATIONS
Required Qualifications:
The requirements listed below are representative of the knowledge, skill and/or ability required.  Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
1.    Bachelor’s degree and four or more years of experience in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering, or equivalent education and related training
2.    Exhibit understanding of statistical methods, including a broad understanding of classical statistics, probability theory, econometrics, time-series, and primary statistical tests
3.    Familiarity with linear algebra concepts for optimization, complex matrix operations, eigenvalue decompositions, and principal components; working knowledge of calculus/differential