Required Skills: PySpark, Databricks, Delta Lake, Spark SQL, AWS, Azure, Snowflake

Job Description

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.

Preferred Qualifications
•    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.