Data Analyst- Wealth Management
· Austin, TX
- On site
- Contract
- $110,000 / year
- Austin, TX
Tailored resume — keyword-matched to this role.
Hiring manager — we find who's hiring.
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Job highlights
- Analyze wealth management data for financial services.
- Utilize advanced SQL and Python for data insights.
- Validate data pipelines and ETL processes.
- Collaborate with stakeholders on data requirements.
- Work with cloud data platforms like Databricks.
About the role
Data Analyst Wealth Management
As a Data Analyst in Wealth Management, you will play a crucial role in extracting, analyzing, and interpreting complex financial data to drive informed business decisions. You will collaborate with stakeholders to understand their needs and translate them into data-driven solutions. This role requires a strong blend of technical expertise in SQL and Python, combined with a solid understanding of wealth management data and processes. **Responsibilities:*** Translate business requirements into data logic and acceptance criteria. * Perform data discovery, profiling, and quality assessment. * Validate data pipelines and ETL processes by reconciling source and target data. * Analyze wealth management data, including holdings, performance, AUM, fees, and transactions. * Utilize Python for data analysis and ad hoc exploration. * Query and analyze large datasets using cloud data platforms like Databricks. * Ensure data governance, quality, and regulatory compliance. * Communicate findings effectively to technical and business audiences. **Required Skills & Qualifications:**- Bachelor's or Master's degree in Finance, Data Science, Business Analytics, or related field.
- 5+ years of experience in a data analyst role within wealth management, asset management, or financial services.
- 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.
- Hands-on experience with PySpark or Databricks (Delta Lake, Spark SQL, notebooks) for large-scale data processing.
- Experience building or contributing to data pipelines, ETL processes, or workflow automation in a financial services context.
- Exposure to custodian data formats and feeds (Schwab, Pershing, Fidelity, etc.) and reconciliation processes.
- Experience with wealth management or portfolio management platforms such as Addepar, Orion, or Black Diamond.
- Familiarity with cloud data platforms such as AWS, Azure, or Snowflake.
- Knowledge of predictive analytics or basic ML applications in financial services (e.g., client segmentation, risk modeling).
- Certifications in data analytics, financial analysis (CFA, CIPM), or cloud platforms are a plus.
- Data Analysis
- SQL
- Python
- Wealth Management Data
- Data Pipelines
- ETL
- Databricks
- Stakeholder Management
- Data Quality
- Financial Services
Skills & topics
- Data Analyst
- Wealth Management
- SQL
- Python
- Financial Services
- Data Analysis
- ETL
- Databricks
- Asset Management
- Business Analytics
How to get hired
- Tailor your resume: Highlight your 5+ years in wealth management data analysis, expert SQL, and Python proficiency. Quantify achievements in data discovery, pipeline validation, and stakeholder communication.
- Showcase relevant experience: Emphasize your understanding of wealth management data (holdings, AUM, fees) and familiarity with platforms like Addepar or Databricks.
- Prepare for technical interviews: Be ready to demonstrate advanced SQL skills with complex queries and Python data manipulation. Practice explaining data governance and regulatory compliance in finance.
- Practice stakeholder communication: Prepare examples of how you translated business needs into data logic and presented findings to diverse audiences.
Technical preparation
Master complex SQL queries and analytical functions.,Practice Python data analysis with pandas/numpy.,Familiarize yourself with Databricks/cloud platforms.,Understand wealth management data structures.
Behavioral questions
Describe a complex data problem you solved.,How do you translate business needs to data?,How do you ensure data quality?,How do you communicate technical findings to non-technical audiences?
Frequently asked questions
- What specific wealth management data is most important for this Data Analyst role?
- For this Data Analyst role in Wealth Management, the most important data points include custodian feeds, portfolio holdings, performance returns, Assets Under Management (AUM), fees, and transaction data. A strong understanding of these areas is critical for success.
- How important are PySpark and Databricks for this Data Analyst position?
- While proficiency in Python (pandas, numpy) is required, hands-on experience with PySpark or Databricks is a preferred qualification. Familiarity with these tools for large-scale data processing and analysis on cloud platforms will significantly enhance your application for this Data Analyst role.
- What level of SQL expertise is expected for the Data Analyst Wealth Management role?
- Expert-level SQL skills are a core requirement for this Data Analyst position. This includes proficiency in complex multi-table joins, CTEs, window functions, subqueries, and designing analytical queries for wealth management data.
- Does this Data Analyst role require direct experience with specific custodian data feeds?
- While a solid understanding of wealth management data is required, direct experience with specific custodian data feeds (like Schwab, Pershing, Fidelity) is listed as a preferred qualification. If you have this experience, be sure to highlight it.
- What are the key communication skills needed for this Data Analyst job?
- Excellent communication and stakeholder management skills are essential. You must be comfortable gathering functional requirements from business stakeholders and clearly presenting your data analysis findings to both technical and non-technical audiences.
- Are there opportunities for growth into data engineering or advanced analytics in this role?
- The preferred qualifications mention experience with data pipelines, ETL processes, and knowledge of predictive analytics or basic ML applications. This suggests potential avenues for growth beyond core data analysis, especially if you demonstrate aptitude in these areas.