Lead Data Analyst – Enterprise Data Analytics

Responsibilities:

Enterprise Data Analysis & Reporting

• Lead complex data analysis and reporting initiatives supporting business, operational, risk, compliance, and regulatory needs.
• Collaborate with stakeholders to gather requirements, define scope, and translate business needs into technical data solutions.
• Perform data extraction, transformation, reconciliation, and validation across multiple enterprise data sources.
• Analyze large and complex datasets to identify trends, investigate discrepancies, and deliver actionable insights.
• Independently manage multiple high-priority analytical requests while ensuring timely and accurate delivery.

Advanced SQL & Data Solutions

• Develop, optimize, and maintain complex SQL queries for data extraction, analysis, reconciliation, and reporting.
• Work with enterprise databases, data warehouses, cloud platforms, and legacy systems.
• Apply advanced SQL techniques, including joins, CTEs, window functions, aggregations, and performance tuning.
• Collaborate with enterprise data teams to understand data structures, business rules, data lineage, and source-to-target mappings.
• Support enterprise data modernization, cloud adoption, and data integration initiatives.

Data Quality & Validation

• Develop and execute data validation and reconciliation processes to ensure data accuracy, completeness, and consistency.
• Investigate data anomalies, perform root cause analysis, and recommend corrective actions.
• Establish reusable analytical methodologies, validation frameworks, and reporting standards.
• Ensure appropriate handling of confidential and sensitive data in accordance with enterprise security and governance requirements.

Technical Leadership & Continuous Improvement

• Serve as a technical subject matter expert in SQL development, data analysis, and troubleshooting.
• Provide technical guidance, review analytical solutions, and mentor other analysts.
• Partner with cross-functional teams to resolve complex data challenges.
• Identify opportunities to improve efficiency through automation, reusable analytical solutions, and process standardization.
• Explore and leverage approved AI-powered tools to support data analysis, SQL development, testing, documentation, and automation.

Required Experience

• Bachelor's degree in Computer Science, Information Technology, Data Analytics, Information Systems, Finance, or a related field, or equivalent practical experience.
• 8–10 years of relevant experience in data analytics, business intelligence, enterprise data management, or related disciplines.
• Advanced hands-on SQL experience, including complex query development, optimization, and troubleshooting.
• Experience analyzing, reconciling, and validating large datasets across multiple systems.
• Experience with enterprise relational databases, data warehouses, or cloud-based analytics platforms.
• Strong analytical, problem-solving, and root cause analysis skills.
• Experience gathering business requirements and translating them into analytical solutions.
• Strong communication, stakeholder management, and technical documentation skills.
• Demonstrated ability to lead complex analytical initiatives and provide technical guidance to other analysts.

Preferred Experience Includes

• Experience in financial services, wealth management, brokerage, capital markets, risk management, or compliance.
• Knowledge of financial datasets, including customer accounts, securities transactions, positions, holdings, and balances.
• Experience with AWS technologies such as Amazon Redshift, S3, Athena, and Glue.
• Experience with ETL/ELT processes, data pipelines, data integration, and data modernization.
• Working knowledge of Python and pandas for data analysis, validation, scripting, and automation.
• Experience with Qlik Sense, Tableau, Power BI, or similar business intelligence platforms.
• Familiarity with data governance, data lineage, source-to-target mapping, and data quality frameworks.
• Experience supporting regulatory reporting, investigations, legal discovery, or audit-related data requests.
• Experience with AI-powered productivity tools such as Microsoft Copilot, ChatGPT, Claude, or similar approved technologies.
• Experience developing automated reporting, reusable queries, and analytical validation processes.