Senior Data Architect- Risk Management Office

Senior Data Architect- Risk Management Office

Job Location:-New York City, NY or Jersey City, NJ (Day 1 Onsite- Candidate Needs to work 5 Days at the Client Office)

Long Term

Note from the Client:- Candidate should have extensive experience and deep domain knowledge of financial services back-office operations, specifically within Risk Management (e.g., trade lifecycle, settlement risk, counterparty data, collateral).

The Role

The Risk Management Back Office is the operational core of the risk ecosystem, responsible for the integrity, settlement, and reporting of all trading activity. This team ensures the foundational data that underpins a firm's risk analysis is accurate, timely, and secure. We are seeking an exceptional Senior Data Architect to lead the design and implementation of a next-generation data platform for our financial services clients, directly enabling robust risk mitigation, regulatory compliance, and operational excellence.

Position Summary

As a Senior Data Architect, you will be the primary owner of the data architecture for the Risk Management Back Office. You will leverage your deep expertise in modern cloud data platforms and financial risk management to design and build scalable, secure, and resilient data solutions on AWS and Snowflake. This role requires a hands-on leader who can architect enterprise-grade systems, establish rigorous data governance and quality frameworks, and collaborate effectively with stakeholders across the front, middle, and back office.

Key Responsibilities

Architect & Design: Design, build, and maintain the end-to-end data architecture for the Risk Management Back Office, leveraging AWS and Snowflake to support critical functions like trade settlement, collateral management, regulatory reporting, and data reconciliation.

Data Modeling & Pipelines: Develop conceptual, logical, and physical data models for risk data domains. Lead the development of complex, high-performance data ingestion and transformation pipelines using Python (including Snowpark) and AWS data services (e.g., Glue, Lambda, Kinesis).

Governance & Quality: Establish and champion a robust data governance framework. Define and enforce standards for data lineage, metadata management, and data quality. Implement monitoring and alerting systems to ensure the highest levels of data integrity.

Business Intelligence & Analytics: Partner with risk analysts, operations teams, and leadership to understand their data needs. Architect data marts and semantic layers optimized for analytics and support the development of insightful dashboards and reports using Sigma, Power BI, and other BI tools.

Security & Compliance: Serve as a subject matter expert on data security within the data platform. Design and implement solutions for data classification, access control, and protection, ensuring compliance with firm policies and financial regulations.

Technical Leadership: Provide technical guidance and mentorship to data engineers and analysts. Champion best practices in data architecture, software engineering, and cloud infrastructure. Drive innovation by evaluating and adopting new technologies and methodologies.

Required Qualifications & Skills

15 years of experience in data architecture, data engineering, or a similar role, with a proven track record of designing and delivering large-scale data solutions.

Extensive, hands-on experience with Snowflake, including performance tuning, security best practices, and cost management.

Expert-level knowledge of the AWS ecosystem, including S3, EC2, Lambda, Glue, IAM, and networking fundamentals.

Advanced programming proficiency in Python for data manipulation, pipeline development, and automation.

Demonstrable experience architecting and delivering data solutions for BI and analytics, with direct experience using tools like Sigma and/or Power BI.

Crucially, extensive experience and deep domain knowledge of financial services back-office operations, specifically within Risk Management (e.g., trade lifecycle, settlement risk, counterparty data, collateral).

Expert-level understanding of data architecture patterns (e.g., Data Warehousing, Data Lake, Data Mesh), data modeling, and data governance principles.

Preferred Qualifications

Deep, practical experience with data security principles and implementation, including data encryption (at-rest, in-transit), tokenization, and managing Material Non-Public Information (MNPI).

Experience with data transformation tools like dbt (Data Build Tool).

Familiarity with infrastructure-as-code (IaC) tools such as Terraform or CloudFormation.

Knowledge of streaming data technologies (e.g., Kafka, Kinesis).

Strong understanding of financial instruments across equities, fixed income, and derivatives

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.