Data Analyst

Senior Data AnalystWealth Management Practice | Data Engineering & AnalyticsDOMAINWealth ManagementTECHNICAL COREAdvanced SQL / STTMCLOUD ENVMS Azure (Preferred)EXPERIENCE5+ Years Senior LevelKey Objective: We are seeking a highly analytical Senior Data Analyst to lead data discovery, quality profiling, andSource-to-Target Mapping (STTM) for enterprise wealth management data platforms. This role serves as the critical bridgebetween wealth business teams and engineering developers. Role OverviewAs a Senior Data Analyst in our Wealth Management technology practice, you will play a central role in shapingclient and portfolio data solutions. You will be responsible for navigating complex legacy and modern datastructures—including client profiles, accounts, holdings, transactions, performance metrics, and advisory billingdata. 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 theprincipal functional contact for ETL/Data Engineers, effectively translating business logic into actionableengineering specifications and facilitating clear walkthroughs. Primary Responsibilities• • • • • • • Data Profiling & Pattern Analysis: Execute complex SQL queries across relational databases, data lakes, andwarehouses to analyze data distribution, evaluate data quality, discover data anomalies, and identify underlyingrelational patterns.Source-to-Target Mapping (STTM): Design, author, and maintain robust, granular STTM documents detailingbusiness rules, field transformations, data types, primary/foreign key relationships, and data pipeline logic.Developer Collaboration & Bridge: Conduct detailed walkthroughs of mapping documents with engineeringteams (ETL/Data Pipeline developers), clarifying edge cases, data constraints, and business intent to drivesmooth implementation.Data Quality & Governance: Establish baseline data quality metrics, define data validation rules, andcollaborate with data governance leads to remediate data discrepancies or gaps across financial datasets.Wealth Management Domain Application: Analyze domain-specific data entities, including householdrelationships, investment portfolios, asset classes, custody positions, fee calculations, and trade histories.Stakeholder Communication: Articulate data insights, structural risks, and mapping dependencies clearly toboth technical developers and non-technical business stakeholders/product owners.Testing & Acceptance Support: Assist QA and engineering teams during sprint cycles by validatingtransformed datasets against original target specifications using customized SQL validation scripts.Confidential - Wealth Management PracticePage 1 of 2Minimum Qualifications• • • • • • Experience: 5+ years of hands-on experience as a Data Analyst, Data Modeler, or Technical Business Analystin enterprise data environment initiatives.Advanced SQL Expertise: Proven mastery in writing complex SQL scripts (multi-table JOINs, CTEs, windowfunctions, 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, structuralinconsistencies, and data integrity issues.Communication Skills: Exceptional verbal and written communication skills with proven experience leadingtechnical specification reviews with software developers and architects.Education: Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Finance, or a relatedquantitative field.Preferred Experience & Skills• • • • Wealth Management Domain Knowledge: Direct experience working with financial, wealth, investmentmanagement, brokerage, or banking data domains (e.g., portfolio management, custodial feeds, advisoryaccounts).MS Azure Cloud Environment: Exposure to or experience working with cloud data platforms on MicrosoftAzure (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, andutilizing tools like Jira or Azure DevOps.Core Skills & CompetenciesAdvanced SQLSource-to-Target Mapping (STTM)Wealth Management DomainJira / Azure DevOpsData LineageConfidential - Wealth Management PracticeData Profiling & QualityMS Azure CloudData Pipeline SpecificationDeveloper CommunicationRelational ModelingPage 2 of 2