Data Architect- Wealth Management

Job Title: Data Architect- Wealth Management

Location: Chicago, Illinois (Hybrid)

VISA: Only H1-B

Must Have Banking Domain EXP

Job Description:

Position Description

Join a collaborative data and analytics team building a modern, cloud native data platform on AWS. In this hands on architecture role, you will shape the data foundations that power wealth management insights, working alongside engineers, analysts, and business stakeholders. You will have the autonomy to define standards and patterns while working with a modern lake house and streaming stack. This hybrid position offers a strong balance of technical depth, influence, and professional growth.

Future Responsibilities:

. Design and maintain the end to end data architecture for the Wealth Management domain, including data lakes/lake houses, warehouses, and streaming pipelines on AWS.

. Define conceptual, logical, and physical data models for core wealth management entities such as clients, households, accounts, portfolios, holdings, transactions, and advisor relationships.

. Establish architecture standards, patterns, and best practices for data ingestion, storage, transformation, and consumption across the organization.

. Architect solutions for regulatory and compliance reporting (e.g., SEC, Client, Reg BI, SOX, and relevant privacy regimes), ensuring auditability and data lineage.

. Lead the design of data governance, quality, cataloging, and master data management (MDM) frameworks.

. Partner with security teams to enforce encryption, access controls, and PII/PCI handling for sensitive financial and client data.

. Guide data engineering teams on implementation, review designs, and ensure alignment to the target architecture.

. Evaluate and select tools and AWS services, balancing cost, performance, scalability, and maintainability.

. Support real time and batch integration with source systems such as portfolio accounting, custody, CRM, market data feeds, and financial planning platforms.

Collaborate with business and analytics stakeholders to enable BI, reporting, and advanced analytics/ML use cases.

Requirements:

. 10–12 years of experience in data engineering / data architecture, with a substantial portion in Banking & Financial Services.

. Demonstrated experience in Wealth Management or closely adjacent areas (asset management, private banking, or brokerage), with working knowledge of financial instruments, portfolios, custody, advisory, and client/household data.

. Proven track record designing large scale data platforms on AWS.

. Strong data modeling expertise across dimensional, data vault, and normalized/3NF approaches, and knowing when to apply each.

. Deep experience with data lake / lakehouse and data warehouse architecture, including ETL/ELT design.

. Solid understanding of data governance, data quality, metadata management, and data lineage in a regulated environment.

. Experience handling sensitive data with appropriate security, privacy, and compliance controls.

. Strong stakeholder management and the ability to communicate architectural decisions to technical and non technical audiences.

. AWS storage & compute: S3, Redshift, EMR, Athena, Glue, Lake Formation; lakehouse formats Parquet and Iceberg or Delta Lake.

. Streaming and serverless orchestration: Kinesis, MSK (Managed Kafka), Lambda, Step Functions, MWAA (Managed Airflow).

. Databases: RDS, Aurora, DynamoDB.

. Security & access: IAM, KMS, encryption at rest/in transit, and fine grained access control.

. Infrastructure as Code (CloudFormation and/or Terraform), advanced SQL, and Python.

. BI / consumption tools: QuickSight, Tableau, or Power BI.

Nice to Have Skills

. AWS certifications (e.g., AWS Certified Data Analytics – Specialty, AWS Certified Solutions Architect).

. Enterprise architecture framework experience (e.g., TOGAF).

. Experience with data cataloging and governance tools (e.g., Collibra, Alation).

. Familiarity with market data providers (e.g., Bloomberg, Refinitiv) and portfolio accounting / performance systems.

. Exposure to machine learning / advanced analytics enablement.

. Experience with data mesh or domain oriented data architecture.

. Knowledge of relevant regulatory frameworks and reporting requirements in wealth management.