Analytics Data Engineer III

Analytics Data Engineer III at Truist Bank in Atlanta, GA (onsite) is responsible for sourcing, analyzing, documenting and maintaining data assets for Truist's Retail Community Bank portfolio and related operations, coordinating across multiple lines of business to drive end-to-end data initiatives.

Responsibilities

• Lead end-to-end work on data sources for projects, including discovery, design, development and ongoing maintenance to meet business requirements; perform analysis, validation and interpretation of outputs; manage issues to resolution with close coordination between line-of-business partners and data engineers.
• Apply deep understanding of quantitative analysis principles to transform data assets for use by decision makers and data scientists across the bank.
• Leverage reporting options such as static reports, OLAP, and dashboards based on specifications; partner with senior management and BI architecture and reporting teams to define data organization, transformations, formatting, OLAP usage and tool selection to achieve reporting objectives.
• Collaborate with LOB analytics groups through ongoing communication and periodic user forums; participate in internal and external forums to stay informed on banking technology advances.
• Develop training materials and user documentation for data and report retrieval; mentor team members and LOB partners on new products and reporting tools; coach colleagues in efficient and accurate coding practices.
• Prioritize ad hoc reporting efforts by setting clear expectations with LOB partners and management.
• Develop solutions and recommendations to improve data integrity; analyze data issues and work with development teams to resolve problems; investigate trends and patterns in complex datasets to determine corrective actions.
• Foster collaboration across organizational levels, engaging mid-level managers to align efforts.

Requirements

• Bachelor's degree and at least 6+ years of experience in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering.
• Proven knowledge of data warehousing concepts and transactional data environments and related technologies.
• Hands-on data engineering experience with the ability to manage large data volumes.
• Familiarity with analytics life cycle methodologies including data cleansing and preparation methods such as regex, filtering, indexing, interpolation and outlier treatment.
• Strong experience with data extraction across environments, including SQL and scripting/tooling variants (e.g., JQuery).
• Proven ability to manage multiple projects with tight deadlines in a collaborative environment.
• Maintains a high level of competency in statistical and analytical principles, tools and techniques.
• Knowledge of database environments such as IBM DB2, Oracle and Netezza; programming skills in SAS, SQL and Toad; exposure to applied data science tools (R, Python, SAS E-Miner); familiarity with BI/visualization tools (Tableau, MicroStrategy); and proficient with Microsoft Office applications (Excel, PowerPoint, Word).

Technologies

• SQL
• JQuery
• IBM DB2
• Oracle
• Netezza
• SAS
• Toad
• R
• Python
• SAS E-Miner
• Tableau
• MicroStrategy
• Excel
• PowerPoint
• Word

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Life insurance
• Disability insurance
• Accidental death and dismemberment
• Tax-preferred savings accounts
• 401k plan
• Vacation days
• Sick days
• Paid holidays
• Defined benefit pension plan
• Restricted stock units
• Deferred compensation plan