Senior Data Analyst – Wealth Management

Role Overview

• Explore and profile large, complex financial datasets to understand structure, lineage, gaps, and anomalies across custodian, portfolio, and transaction data.
• Identify data relationships, patterns, and inconsistencies across source systems to inform data mapping, transformation logic, and business rules.
• Conduct deep-dive analysis on wealth management data — including positions, returns, benchmarks, fees, and cash flows — to validate completeness and accuracy.
• Document data dictionaries, field definitions, and business logic for use by both technical and non-technical teams.
• Investigate data quality issues end-to-end, trace root causes across source systems, and recommend remediation approaches.
• Engage directly with business stakeholders — advisors, portfolio managers, operations, and compliance — to gather, analyze, and document functional data requirements.
• Translate business requirements into precise data logic, transformation rules, and acceptance criteria for downstream development and reporting.
• Define and formalize calculation logic for KPIs such as AUM, performance returns, fee schedules, and client segmentation.
• Review and validate business logic implemented in pipelines, data models, and reports to ensure alignment with requirements.
• Act as a bridge between business teams and technology, ensuring data solutions are grounded in real operational needs.
• Write complex SQL queries — including CTEs, window functions, and aggregations — to analyze datasets, build reusable logic, and support reporting and validation needs.
• Validate pipeline outputs by querying source and target systems, reconciling counts, amounts, and key metrics to confirm data integrity.
• Develop test cases and validation scripts to verify transformation logic, business rules, and data completeness after pipeline runs.
• Use Python and/or Databricks notebooks for ad hoc data analysis, profiling, and validation where scale or complexity requires it.
• Collaborate with engineering teams to review transformation logic, flag discrepancies, and verify that implemented pipelines match documented requirements.
• Develop and maintain dashboards, reports, and KPI frameworks to support advisors, portfolio managers, and leadership.
• Support client segmentation, performance reporting, AUM analysis, and investment strategy analysis.
• Translate complex financial data findings into clear, concise narratives and recommendations for non-technical audiences.
• Ensure all reporting outputs comply with financial regulations and internal data governance standards.

Requirements

• Bachelor's or Master's degree in Finance, Data Science, Business Analytics, or related field.
• 8-10+ years of experience in a data analyst role within wealth management, asset management, or institutional investments.
• Expert-level SQL skills — complex multi-table joins, CTEs, window functions, subqueries, and analytical query design.
• Strong ability to gather and analyze functional requirements from business stakeholders and translate them into data logic and acceptance criteria.
• Proven experience with data discovery and profiling — understanding data structures, identifying quality issues, and documenting findings clearly.
• Experience validating data pipelines or ETL outputs — reconciling source vs. target data, verifying business logic, and writing test cases.
• Solid understanding of wealth management data — custodian feeds, portfolio holdings, performance returns, AUM, fees, and transactions.
• Proficiency with Python for data analysis and ad hoc exploration (pandas, numpy); PySpark experience is a plus.
• Familiarity with Databricks or similar cloud data platforms for querying and analyzing large datasets.
• Understanding of data governance, data quality frameworks, and regulatory compliance in financial services.
• Excellent communication and stakeholder management skills — comfortable presenting findings to both technical and business audiences.

Tech Stack

• Cloud
• ETL
• Numpy
• Pandas
• PySpark
• Python
• SQL