Principal Data Analyst

Fidelity Investments•Jersey City, NJ

3d•$169,541 - $181,000•Hybrid

About The Position

Position Description: Creates scalable documented data design solutions using PL/SQL and Extract, Transform, Load (ETL) tools. Provides data design solutions for cross program initiatives. Develops data quality review strategies. Advocates for adoption of Data Application Programming Interface (API) adoption and reuse. Innovates the product development process to allow the company to continually improve and maintain market leadership in the market. Analyzes user requirements, procedures, and problems to automate or improve existing systems -- reviewing computer system capabilities, workflow, and scheduling limitations.

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and five (5) years of experience as a Principal Data Analyst (or closely related occupation) performing data analysis and design modeling in a financial services environment.
Or, alternatively, Master’s degree in Computer Science, Engineering, Information Technology, Information Systems, or a closely related field (or foreign education equivalent) and three (3) years of experience as a Principal Data Analyst (or closely related occupation) performing data analysis and design modeling in a financial services environment.
Demonstrated Expertise (“DE”) performing data analytics for business users based on enterprise requirements, using SQL analytical functions and Informatica Enterprise Data Catalog (EDC); and creating and implementing data quality checks to ensure the accuracy of data for business use, using Informatica Data Quality (IDQ).
DE creating Entity-Relationship (ER) models and conceptual data models for Data Warehousing (DW) (to be presented to information architects), logical models (for business stakeholders), and physical data models (to be implemented in databases), using SAP Power Designer; proposing, designing, and implementing ER or DW models based on business requirements; and generating Data Definition Languages (DDLs) for physical implementation of tables, using SAP Power Designer.
DE acting as the liaison between business and technology partners to translate business requirements into technical design solutions; creating business requirement documents in Microsoft Word, to document requests from business partners and define the technical components of work to be done by data engineering teams; and collaborating with technology partners to analyze data and create a Source-To-Target (STM) mapping document, using TOAD in MS Excel.
DE planning, designing, and implementing Data Application Programming Interface (API) signatures for RESTful APIs, using Open API Specification (OAS); creating, editing, and organizing API definitions, using the Swagger Hub platform; and performing API socialization using Swagger Hub API exploratory features.

Responsibilities

Defines and leads enterprise-level data strategy and analytics initiatives.
Develops and implements advanced analytical frameworks and advises senior leadership on data-driven decision-making.
Performs independent and complex technical and functional analysis for multiple divisional initiatives.
Develops innovative insights to support strategic business goals.
Enforces data modeling and data design practices; and delivers physical database design from logical models.
Models data defining conceptual data models, focusing on business data needs across subject areas.
Participates in requirement and analysis sessions with business and technical staff to analyze data requirements.
Develops platforms for data projects; and provides end-to-end solutions and design for projects.
Designs data catalog products and maintains factsheets; and provides data design solutions for cross program initiatives.
Promotes Data API Adoption and reuse.
Increases data fluency and literacy across business units.
Identifies opportunities to leverage enterprise data model for new projects, improve efficiencies, and eliminate data redundancy.
Mentors junior analysts.