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# Data Analyst

- **Company:** AGILE GRID SOLUTIONS LLC
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Software Bug Management, Cloud Database, Data Governance, Data Visualization, Database Storage Structures, Decision Support Systems, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, E2e Testing, SQL Databases, Tableau (Software), Cloud Platform System, Feature Engineering, Microsoft Power Automate, Delivery Pipeline, Deep Learning, Pandas, Scikit Learn, Information Technology, Data Lineage, Vba Programming Language, Spacy, Data Pipelines, Powerapps, Alteryx
- **Published:** October 22, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pfdyzldc8r

## About the Role

The ideal candidate will possess a bachelor's degree in Computer Science, Data Science, Statistics, or a related field. A master's degree or higher is preferred. Extensive experience in data analysis, machine learning, and statistical modeling is essential. Proficiency in Python, SQL, and data visualization tools such as Tableau is required. Familiarity with data governance, regulatory reporting, and cloud-based data environments will be advantageous. Strong problem-solving skills, attention to detail, and the ability to communicate complex technical information effectively are critical. Candidates should demonstrate leadership qualities, strategic thinking, and the ability to work collaboratively in a fast-paced environment.

## Description

The purpose of this role is to enable data-driven strategic and operational decision-making within Barclays by extracting actionable insights from large and complex datasets. As a Data Analyst-AVP, you will be responsible for performing advanced analytics, developing predictive models, and creating visualizations that support regulatory compliance, risk management, and operational efficiency. This position requires a blend of technical expertise, analytical thinking, and collaborative skills to translate data into meaningful business insights. You will work closely with cross-functional teams to automate reporting processes, improve data quality, and enhance decision support systems, ensuring regulatory requirements are met with precision and timeliness., * Develop, implement, and apply machine learning, AI, NLP, deep learning, and predictive analytics models to support regulatory insights, risk identification, and data quality trend analysis.

 * Build, train, evaluate, and document models using Python and associated libraries such as pandas, NumPy, scikit-learn, and SpaCy.
 * Integrate analytical models into reporting workflows, automation pipelines, and decision support tools, performing feature engineering as needed.
 * Analyze existing reporting and operational processes to identify automation opportunities, enhancing efficiency and scalability.
 * Automate manual processes using Python, VBA, Alteryx, and Microsoft Power Platform tools like Power Automate and PowerApps.
 * Standardize processes to improve reliability, control effectiveness, and capacity management.
 * Create interactive dashboards and visual reports using Tableau to facilitate stakeholder understanding and decision-making.
 * Source, prepare, validate, and transform data for regulatory reporting, maintaining comprehensive documentation of data lineage, logic, and controls.
 * Build automated, repeatable pipelines for regulatory reporting utilizing Python, SQL, and Tableau.
 * Apply data governance best practices, including data quality controls, validation checks, and adherence to security and regulatory policies.
 * Support audit and regulatory examinations through detailed documentation, methodology explanations, and evidence collection.
 * Maintain and optimize database structures and queries supporting reporting and analytics across relational and cloud environments.
 * Prepare technical documentation for data pipelines, models, and reporting processes.
 * Conduct end-to-end testing, including validation, test planning, user acceptance testing, defect management, and deployment readiness.