Data Analyst

Senior Data Analyst Wealth Management Practice | Data Engineering & Analytics DOMAIN Wealth Management TECHNICAL CORE Advanced SQL / STTM CLOUD ENV MS Azure (Preferred) EXPERIENCE 5+ Years Senior Level Key Objective: We are seeking a highly analytical Senior Data Analyst to lead data discovery, quality profiling, and Source-to-Target Mapping (STTM) for enterprise wealth management data platforms. This role serves as the critical bridge between wealth business teams and engineering developers. Role Overview 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. Primary Responsibilities • • • • • • • 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. Confidential - Wealth Management Practice Page 1 of 2 Minimum Qualifications • • • • • • 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. Preferred Experience & Skills • • • • 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, and utilizing tools like Jira or Azure DevOps. Core Skills & Competencies Advanced SQL Source-to-Target Mapping (STTM) Wealth Management Domain Jira / Azure DevOps Data Lineage Confidential - Wealth Management Practice Data Profiling & Quality MS Azure Cloud Data Pipeline Specification Developer Communication Relational Modeling Page 2 of 2