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

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• 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

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Minimum Qualifications

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• 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

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• 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

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