We are seeking a highly skilled Data Analyst – Wealth Management to join our growing team in Austin. This is a discovery- and analysis-driven role for a curious, detail-oriented professional who thrives on understanding complex financial data, translating business needs into clear data logic, and surfacing insights that drive decisions.
The ideal candidate excels at writing sophisticated SQL queries, analyzing and profiling large datasets, defining business logic, and validating data across wealth management systems. You will partner closely with investment teams, operations, technology, and business stakeholders to understand functional requirements and ensure data is accurate, consistent, and fit for purpose. Hands-on experience with Python and Databricks is a plus, but this role is fundamentally about analytical depth and business understanding — not pipeline engineering.
Key Responsibilities
Data Discovery & Profiling
- 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.
Requirements Analysis & Business Logic
- 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.
Query Development & Pipeline Validation
- 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, reconcilin