Truist · 6 hours ago

CSBB Analytics Engineer

Charlotte, NC

Full-time

Remote

Mid, Senior Level

5+ years exp

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Responsibilities

Lead research, analyze, design, develop and/or maintain data sources in support of projects, information needs and business requirements. Perform analysis, validation and interpretation of outputs. Take ownership of issues through resolution, including close coordination between LOB partners and data engineers.

Have a deep understanding of various quantitative analysis principles and ability to transform data assets to aid in consumption by decision & data scientists across the bank.

Work closely with team members and BI Architecture and Reporting Managers concerning data organization, transformations, formatting, OLAP, tool choices etc. to define and implement reporting objectives.

Prioritize and manage ad hoc reporting efforts by providing clear expectations to LOB partners and management.

Develop solutions and recommendations for improving data integrity issues. Analyze data issues and work with development teams for problem resolutions. Identify problematic areas and conduct research to determine the best course of action to correct the data, identify, analyze and interpret trends and patterns in complex datasets.

Foster communication and partnership across multiple levels of the organization including engagement with line of business contributors and junior-level managers.

Qualification

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Data engineeringData warehousingSQLStatistical principlesData visualizationPythonRSASQuantitative analysisData cleansingMicrosoft Office SuiteIBM DB2OracleNetezzaSAS E-MinerTableauMicroStrategyJQueryProject management

Required

Bachelor's degree and 5+ years of experience in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering.

Demonstrated knowledge and skill in data warehousing and transactional application data concepts and technology.

Proven experience with data engineering and ability to manage large data volumes.

Candidate should demonstrate understanding of data analytics life cycle methodologies including data cleansing and preparation methodologies, including regex, filtering, indexing, interpolation, and outlier treatment.

Strong familiarity with data extraction in a variety of environments (e.g., SQL, JQuery, etc.).

Experience in managing multiple projects with tight deadlines in a collaborative environment.

Maintain a high level of competency in statistical and analytical principles, tools, and techniques.

Incumbent should have an understanding of various database environments (IBM DB2, Oracle, Netezza), technical programming skills (SAS, SQL, Toad), exposure to applied data science tools (R, Python, SAS E-Miner), familiarity with data visualization and BI tools (Tableau, MicroStrategy), and demonstrated proficiency in Microsoft Office Suite (Excel, PowerPoint, Word)