Data Analyst-AVP

Barclays•Hanover Township, NJ

1d•$144,414 - $155,000•Onsite

About The Position

The purpose of this role is to enable data-driven strategic and operational decision-making through extracting actionable insights from large datasets, performing statistical and advanced analytics to uncover trends and patterns, and presenting findings through clear visualizations and reports. The Assistant Vice President (AVP) level expectations include advising and influencing decision-making, contributing to policy development, and taking responsibility for operational effectiveness. This role involves collaborating closely with other functions and business divisions, and potentially leading a team to deliver on work that impacts the entire business function. For individual contributors, the role involves leading collaborative assignments, guiding team members, identifying the need for specialized expertise, and exploring new directions for projects. The role also requires consulting on complex issues, advising People Leaders on escalated issues, identifying ways to mitigate risk, developing new policies/procedures, and taking ownership for managing risk and strengthening controls. Collaboration with other areas of work is essential to stay updated on business activity and strategy. The role demands engaging in complex analysis of data from multiple internal and external sources to solve problems creatively and effectively, and communicating complex information clearly to stakeholders. All colleagues are expected to demonstrate Barclays Values (Respect, Integrity, Service, Excellence, Stewardship) and the Barclays Mindset (Empower, Challenge, Drive).

Requirements

Develop, implement, and apply ML/AI/NLP/Deep Learning, Predictive Analytics, and Forecasting Models
Build, train, test, evaluate, and document models using Python and associated analytical libraries (e.g., pandas, NumPy, scikit learn, SpaCy)
Integrate models into reporting workflows, automation pipelines, or decision support tools
Perform feature engineering
Analyze current reporting/operational processes to identify opportunities for automation and efficiency gains
Replace manual steps with end to end automated workflows using Python, VBA, Alteryx, and Microsoft Power Platform (Power Automate/PowerApps)
Standardize processes to improve scalability, capacity, reliability, and control effectiveness
Build interactive dashboards using Tableau
Source, prepare, validate, and transform data for regulatory reporting
Maintain logic, lineage, and documentation in accordance with internal controls and external regulatory expectations
Build automated, repeatable regulatory reporting pipelines using Python, SQL, and Tableau
Apply data governance practices, including data quality controls, validation checks, metadata and lineage management, and adherence to policies for data integrity, security, accessibility, and regulatory compliance
Provide audit and regulatory examination support, including documentation, methodology explanations, evidence for controls testing, and responses to risk/compliance inquiries
Maintain and optimize database structures and queries supporting regulatory reporting and analytics across relational and/or cloud environments
Prepare technical documentation for regulatory reporting processes, automated workflows, data pipelines, and analytical models
Conduct end to end testing, including requirements validation, test planning, test case execution, UAT coordination, defect management, and production deployment readiness

Nice To Haves

Leveraging machine learning/AI
Using VBA, Alteryx, and Microsoft Power Platform (Power Automate/PowerApps)
Experience with cloud environments

Responsibilities

Investigate and analyze data issues related to quality, lineage, controls, and authoritative source identification.
Document data sources, methodologies, and quality findings with recommendations for improvement.
Design and build data pipelines to automate data movement and processing.
Apply advanced analytical techniques to large datasets to uncover trends and correlations.
Develop validated logical data models.
Translate insights into actionable business recommendations that drive operational and process improvements, leveraging machine learning/AI.
Design and create interactive dashboards and visual reports using applicable tools.
Automate reporting processes for regular and ad-hoc stakeholder needs.
Develop, implement, and apply ML/AI/NLP/Deep Learning, Predictive Analytics, and Forecasting Models to support regulatory insights, data quality trend analysis, and risk identification.
Build, train, test, evaluate, and document models using Python and associated analytical libraries (e.g., pandas, NumPy, scikit learn, SpaCy).
Integrate models into reporting workflows, automation pipelines, or decision support tools; perform feature engineering.
Analyze current reporting/operational processes to identify opportunities for automation and efficiency gains.
Replace manual steps with end-to-end automated workflows using Python, VBA, Alteryx, and Microsoft Power Platform (Power Automate/PowerApps).
Standardize processes to improve scalability, capacity, reliability, and control effectiveness.
Build interactive dashboards using Tableau.
Source, prepare, validate, and transform data for regulatory reporting; maintain logic, lineage, and documentation in accordance with internal controls and external regulatory expectations.
Build automated, repeatable regulatory reporting pipelines using Python, SQL, and Tableau.
Apply data governance practices, including data quality controls, validation checks, metadata and lineage management, and adherence to policies for data integrity, security, accessibility, and regulatory compliance.
Provide audit and regulatory examination support, including documentation, methodology explanations, evidence for controls testing, and responses to risk/compliance inquiries.
Maintain and optimize database structures and queries supporting regulatory reporting and analytics across relational and/or cloud environments.
Prepare technical documentation for regulatory reporting processes, automated workflows, data pipelines, and analytical models.
Conduct end-to-end testing, including requirements validation, test planning, test case execution, UAT coordination, defect management, and production deployment readiness.