Qode
Wealth Management
SeniorDataAnalyst-WealthManagement
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optimal for Senior candidates.
“Senior Data Analyst - Wealth Management at Qode. Skills: Wealth management data, Complex SQL queries, Data analysis, Business logic translation. Explore and profile financial datasets. Identify data relationships and patterns”
Industry & Context.
Root cause analysis; Data quality issue investigation
What They're Looking For.
Must Have
Bachelor's or Master's degree, 8-10+ years of experience, Expert-level SQL skills, Gather and analyze functional requirements, Data discovery and profiling experience, Experience validating data pipelines, Solid understanding of wealth management data, Proficiency with Python for data analysis, Familiarity with Databricks or similar, Understanding of data governance, Excellent communication and stakeholder management
Nice to Have
Hands-on experience with PySpark or Databricks, Experience building or contributing to data pipelines, Exposure to custodian data formats, Experience with wealth management platforms, Familiarity with cloud data platforms, Knowledge of predictive analytics, Certifications in data analytics, Certifications in financial analysis, Certifications in cloud platforms
What You'll Do.
Explore and profile financial datasets
Identify data relationships and patterns
Conduct deep-dive analysis on data
Document data dictionaries and definitions
Investigate data quality issues
Trace root causes of data issues
Recommend remediation approaches
Gather and document functional data requirements
Translate business requirements into data logic
Define and formalize calculation logic for KPIs
Review and validate business logic
Write complex SQL queries
Validate pipeline outputs
Develop test cases and validation scripts
Use Python or Databricks notebooks for analysis
Collaborate with engineering teams on logic
Develop and maintain dashboards and reports
Support client segmentation analysis
Support performance reporting analysis
Support investment strategy analysis
Translate data findings into narratives
Ensure reporting compliance with regulations
How You'll Work.
Team & Collaboration
Partner with investment teams; Partner with operations; Partner with technology; Partner with business stakeholders; Engage with business stakeholders; Collaborate with engineering teams
Communication Scope
Stakeholder management; Presenting findings; Clear narratives
Full Job Description
**Job Title:** Senior Data Analyst - Wealth Management **Locations:** Austin, TX **Type:** Full-Time **Company Overview** Incedo is a US-based consulting, data science and technology services firm with over 4000 people helping clients from our six offices across US, Mexico and India. We help our clients achieve competitive advantage through end-to-end digital transformation. Our uniqueness lies in bringing together strong engineering, data science, and design capabilities coupled with deep domain understanding. We combine services and products to maximize business impact for our clients in telecom, Banking, Wealth Management, product engineering and life science& healthcare industries. **Position Overview** We are seeking a highly skilled Data Analyst – Wealth Management to join our growing team in Austin. This is a discovery- and analysis-driven role for a curious, detail-oriented professional who thrives on understanding complex financial data, translating business needs into clear data logic, and surfacing insights that drive decisions. The ideal candidate excels at writing sophisticated SQL queries, analyzing and profiling large datasets, defining business logic, and validating data across wealth management systems. You will partner closely with investment teams, operations, technology, and business stakeholders to understand functional requirements and ensure data is accurate, consistent, and fit for purpose. Hands-on experience with Python and Databricks is a plus, but this role is fundamentally about analytical depth and business understanding — not pipeline engineering. **Key Responsibilities** Data Discovery & Profiling * Explore and profile large, complex financial datasets to understand structure, lineage, gaps, and anomalies across custodian, portfolio, and transaction data. * Identify data relationships, patterns, and inconsistencies across source systems to inform data mapping, transformation logic, and business rules. * Conduct deep-dive analysis on w
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