Afternoon Data Analyst R Programming Jobs in Berkeley, MO

Senior Data Analyst Wealth Management PracticeData 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