Sr Data Engineer (asset - wealth Management)

Sr Data Engineer – Asset/Wealth Management

Location: Scottsdale, AZ (Onsite/Hybrid 3 days a week)

Exp Level- 9-12 years

About the Role: We are seeking a Data Engineer with strong knowledge of Asset Management data and hands-on experience in ETL and data transformation. The ideal candidate will have a solid understanding of how data flows across asset management systems, including performance data, and will be comfortable working with transformation tools and programming languages such as Python, SQL, or C#.

Key Responsibilities:

• Design, develop, and maintain robust ETL pipelines for Asset Management data.
• Work closely with business and data teams to understand data sources, flows, and transformations related to performance and investment data.
• Implement efficient data transformation processes using both traditional and AI-assisted tools.
• Ensure data accuracy, consistency, and reliability across systems and workflows.
• Optimize data processing and storage solutions for scalability and performance.
• Collaborate with cross-functional teams to define and document data architecture and integration requirements.

Required Skills & Qualifications:

• Strong understanding of Asset Management data (e.g., holdings, transactions, benchmarks, performance, attribution, etc.).
• Proven experience in ETL development, with a focus on data transformation.
• Familiarity with data transformation and integration tools, including exposure to AI-based automation tools (preferred).
• Proficiency in one or more of the following languages: Python, SQL, or C#.
• Experience with relational databases and data modeling concepts.
• Strong analytical and problem-solving skills with attention to detail.
• Ability to work independently and communicate effectively with both technical and non-technical stakeholders.

Preferred Qualifications:

• Prior experience in Financial Services or Asset Management domain.
• Knowledge of cloud-based data platforms (e.g., AWS, Azure, GCP).
• Familiarity with data governance and metadata management best practice.