Senior Data Analyst

As a Senior Data Analyst in our Wealth Management technology practice, you will play a central role in shaping client and portfolio data solutions. You will be responsible for navigating complex legacy and modern data structures- including client profiles, accounts, holdings, transactions, performance metrics, and advisory billing data. You will perform deep-dive data profiling and pattern analysis using advanced SQL, assess data health and quality, and author comprehensive Source-to-Target Mapping (STTM) documentation.

Crucially, you will act as the principal functional contact for ETL/Data Engineers, effectively translating business logic into actionable engineering specifications and facilitating clear walkthroughs.

Data Profiling & Pattern Analysis:
Execute complex SQL queries across relational databases, data lakes, and warehouses to analyze data distribution, evaluate data quality, discover data anomalies, and identify underlying relational patterns. Source-to-Target Mapping (STTM):
Design, author, and maintain robust, granular STTM documents detailing business rules, field transformations, data types, primary/foreign key relationships, and data pipeline logic. Developer Collaboration & Bridge:
Conduct detailed walkthroughs of mapping documents with engineering teams (ETL/Data Pipeline developers), clarifying edge cases, data constraints, and business intent to drive smooth implementation. Data Quality & Governance:
Establish baseline data quality metrics, define data validation rules, and collaborate with data governance leads to remediate data discrepancies or gaps across financial datasets. Wealth Management Domain Application:
Analyze domain-specific data entities, including household relationships, investment portfolios, asset classes, custody positions, fee calculations, and trade histories. Stakeholder Communication:
Articulate data insights, structural risks, and mapping dependencies clearly to both technical developers and non-technical business stakeholders/product owners. Testing & Acceptance Support:
Assist QA and engineering teams during sprint cycles by validating transformed datasets against original target specifications using customized SQL validation scripts

Experience:

5+ years of hands-on experience as a Data Analyst, Data Modeler, or Technical Business Analyst in enterprise data environment initiatives. Advanced SQL Expertise:
Proven mastery in writing complex SQL scripts (multi-table JOINs, CTEs, window functions, subqueries, and analytical functions) for data extraction and profiling. STTM Documentation:
Demonstrated experience creating explicit, comprehensive Source-to-Target Mappings (STTM) for ETL/ELT pipelines, reporting, or data warehouse migrations. Data Quality & Profiling:
Strong background in identifying data anomalies, missingness, structural inconsistencies, and data integrity issues. Communication

Skills:

Exceptional verbal and written communication skills with proven experience leading technical specification reviews with software developers and architects.

Education:

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Finance, or a related quantitative field.

• Wealth Management Domain Knowledge:
  Direct experience working with financial, wealth, investment management, brokerage, or banking data domains (e.g., portfolio management, custodial feeds, advisory accounts).
• MS Azure Cloud Environment:
  Exposure to or experience working with cloud data platforms on Microsoft Azure (e.g., Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, or Databricks on Azure).
• Modern Data Stacks:
  Familiarity with modern data modeling concepts (Dimensional, Snowflake, Data Vault) and orchestration workflows.
• Agile/Scrum Framework:
  Experience working in Agile/Scrum delivery models, managing user stories.