Data Management Specialist

Citizens Financial Group•Johnston, RI

1d•Hybrid

About The Position

Enterprise Data and Analytics is seeking a Junior Business Data Analyst to support the design, development, and continuous improvement of operational, analytical and AI-ready data solutions and data products. This role is ideal for an early-career professional who understands foundational data management, business analysis, semantic modeling, and AI-ready data concepts, and the characteristics of high-quality data required to support analytics and AI at scale. The analyst will participate across the full data product development lifecycle — from business discovery and requirement definition through semantic model design, testing, governance alignment, release, adoption, and continuous improvement. The role will work closely with business stakeholders, data product owners, data engineers, analytics teams, architects, governance teams, and AI practitioners to ensure data products are reusable, trusted, scalable, and aligned to business outcomes. This role also requires comfort using AI-assisted tools to accelerate product design, business analysis, metadata development, documentation, insight generation, and analytical exploration.

Requirements

Foundational understanding of data management principles, including data quality, metadata, lineage, governance, and data lifecycle concepts.
Advanced knowledge of with AI-ready data concepts such as data standardization, feature usability, data consistency, explainability support, and trustworthy data inputs for AI/ML.
Familiarity with semantic models, knowledge graphs, business-friendly data structures, dimensions, measures, hierarchies, and KPI definitions.
Basic understanding of the data product mindset, including designing reusable, consumable, and value-driven data assets.
Knowledge of how data supports BI, analytics, and AI use cases.
Strong analytical and problem-solving skills with the ability to interpret business needs and translate them into data requirements.
Experience working with SQL for querying and validating data.
Familiarity with data visualization and BI tools such as Power BI, Tableau, or similar platforms.
Exposure to data modeling, data mapping, relational databases, and no-SQL databases.
Solid understanding of data structures, normalization, joins, transformations, and source-to-target data flows.
Experience with using AI data analysis and productivity tools.
Strong written and verbal communication skills.
Ability to document requirements, data definitions, mappings, and process flows clearly and accurately.
Comfortable working with both business and technical teams.
Strong attention to detail and commitment to data accuracy and consistency.
Ability to manage multiple tasks and work in a structured, organized manner.
Eagerness to learn and grow in data products, analytics, and AI-enabled data capabilities.
Experience with Agile development.
Bachelor’s degree in Business Analytics, Information Management, Data Analytics, Computer Science, or a related field.
0–3 years of experience in business analysis, data analysis, data management, semantic modeling, reporting, or analytics.
Internship, academic project, or early-career experience in data, reporting, or business intelligence.

Nice To Haves

Awareness of cloud data platforms, modern data architectures, or lakehouse/warehouse concepts is a plus.
Experience with AWS cloud native services such as S3, Redshift, Glue, Athena, Sagemaker, etc. is a plus.
Familiarity with Python, Excel, or data analysis tools and techniques is a plus.
Banking experience preferred.

Responsibilities

Support the design and delivery of data products that enable reporting, analytics, operational insights, and AI/ML use cases.
Participate in the end-to-end data product lifecycle, including discovery, requirements gathering, design, development support, testing, deployment, and ongoing enhancement.
Work with stakeholders to understand business goals, decisions, and processes that the data product must support.
Assist in defining data product use cases, business rules, success measures, and consumption patterns.
Translate business needs into clear data, reporting, and analytical requirements.
Contribute to the creation of AI-ready datasets by making sure data are structured, standardized, documented, and fit for downstream analytics and AI use.
Help define the data product taxonomy and ontology, and identify key entities, relationships, hierarchies, dimensions, attributes, and metrics needed to support semantic and analytical models.
Contribute to logical data modeling and analytical model documentation.
Help align data structures to domain concepts and business terminology.
Assist in defining and documenting semantic models that make data easier for business users, analysts, and AI solutions to understand and consume.
Work with technical teams to ensure semantic layers reflect business logic accurately and consistently.
Analyze data sources and prepare source-to-target mapping documents and functional specifications for data and BI engineers to build ETL jobs, APIs and reports.
Perform data quality and gap analysis and support root cause identification and remediation tracking.
Define data quality rules for ongoing monitoring.
Support test case development and user acceptance testing for data products, reports, and semantic models.
Validate outputs against business requirements and expected results.
Coordinate code deployment /configuration changes to upper environments.
Follow change management stipulations for production implementation and strictly adhere to the compliance and regulatory requirements.
Support efforts to improve data quality, completeness, consistency, and usability for operational, analytical and AI applications.
Partner with the Data Governance team in efforts related to data cataloging, business glossary, and metadata management.
Create data lineage maps and data transfer control requirements documentation and assist the data engineers with data transfer control implementation and testing.
Collaborate with stakeholders to gather all necessary details for documenting the Data Product Charters, User Guides, SLAs and to publish the data products in the enterprise Data Marketplace.
Help monitor adoption, usability, and feedback on delivered data products.
Prepare clear documentation, presentations, and status updates for stakeholders.
Innovate by leveraging agile techniques to achieve efficiency, improve processes and continuously boost productivity.