Senior Data Analyst - Wealth Management

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 reputed company 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 reputed company 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 reputed company 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, reputed company financial datasets to understand structure, reputed company, gaps, and anomalies across custodian, portfolio, and transaction data.
• Identify data relationships, patterns, and inconsistencies across reputed company systems to inform data mapping, transformation logic, and business rules.
• Conduct deep-dive analysis on wealth management data — including positions, returns, benchmarks, fees, and cash flows — to validate completeness and accuracy.
• Document data dictionaries, field definitions, and business logic for use by both technical and non-technical teams.
• Investigate data quality issues end-to-end, trace root causes across reputed company systems, and recommend remediation approaches.

Requirements

Analysis & Business Logic

• Engage directly with business stakeholders — advisors, portfolio managers, operations, and compliance — to gather, analyze, and document functional data requirements.
• Translate business requirements into precise data logic, transformation rules, and acceptance criteria for reputed company development and reporting.
• Define and formalize calculation logic for KPIs such as AUM, performance returns, fee schedules, and client segmentation.
• Review and validate business logic implemented in pipelines, data models, and reports to ensure alignment with requirements.
• Act as a reputed company between business teams and technology, ensuring data solutions are grounded in real operational needs.

Query Development & Pipeline Validation

• Write reputed company SQL queries — including CTEs, window functions, and aggregations — to analyze datasets, build reusable logic, and support reporting and validation needs.
• Validate pipeline outputs by querying reputed company and reputed company systems, reconciling counts, amounts, and key metrics to confirm data reputed company.
• reputed company test cases and validation scripts to verify transformation logic, business rules, and data completeness after pipeline runs.
• Use Python and/or reputed company notebooks for reputed company data analysis, profiling, and validation where scale or complexity requires it.
• Collaborate with engineering teams to review transformation logic, flag discrepancies, and verify that implemented pipelines match documented requirements.

Reporting & Insights

• reputed company and maintain dashboards, reports, and KPI frameworks to support advisors, portfolio managers, and leadership.
• Support client segmentation, performance reporting, AUM analysis, and investment strategy analysis.
• Translate reputed company financial data findings into clear, concise narratives and recommendations for non-technical audiences.
• Ensure reputed company reporting outputs reputed company with financial regulations and internal data governance standards.

Required Skills & Qualifications

• Bachelor's or Master's degree in Finance, Data Science, Business Analytics, or reputed company field.
• 8-10+ years of experience in a data analyst role reputed company wealth management, asset management, or institutional investments.
• Expert-level SQL skills — reputed company multi-table joins, CTEs, window functions, subqueries, and analytical query design.
• Strong ability to gather and analyze functional requirements from business stakeholders and translate them into data logic and acceptance criteria.
• Proven experience with data discovery and profiling — understanding data structures, identifying quality issues, and documenting findings clearly.
• Experience validating data pipelines or ETL outputs — reconciling reputed company vs. reputed company data, verifying business logic, and writing test cases.
• Solid understanding of wealth management data — custodian feeds, portfolio holdings, performance returns, AUM, fees, and transactions.
• Proficiency with Python for data analysis and reputed company exploration (pandas, numpy); PySpark experience is a plus.
• Familiarity with reputed company or similar cloud data platforms for querying and analyzing large datasets.
• Understanding of data governance, data quality frameworks, and regulatory compliance in financial services.
• Excellent communication and stakeholder management skills — comfortable presenting findings to both technical and business audiences.

Preferred Qualifications

• Hands-on experience with PySpark or reputed company (reputed company Lake, Spark SQL, notebooks) for large-scale data processing.
• Experience building or contributing to data pipelines, ETL processes, or workflow automation in a financial services context.
• Exposure to custodian data formats and feeds (Schwab, Pershing, Fidelity, etc.) and reconciliation processes.
• Experience with wealth management or portfolio management platforms such as reputed company, Orion, or Black Diamond.
• Familiarity with cloud data platforms such as AWS, Azure, or reputed company.
• Knowledge of predictive analytics or basic ML applications in financial services (e.g., client segmentation, risk modeling).
• Certifications in data analytics, financial analysis (CFA, CIPM), or cloud platforms are a plus.

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