Data Architecture Market Risk Lead

BRAINS WORKGROUP, INC.; New York (NY); Hybrid; USD 200,000 - 240,000; Full time

Job summary

Unknown Bank in New York is seeking a Data Architecture & Engineering Lead for Market Risk Technology. Hybrid work arrangement with two days in the office every two weeks, and a salary range of $200k–$240k plus a 20% bonus plus benefits.

You will oversee end-to-end data platform design, migration to Databricks/Delta Lake, and platform operations for VaR, FRTB, sensitivities, P&L, and regulatory reporting. Lead data models, performance tuning, and data observability across Risk, Finance, and

Qualifications

• 15+ years in data architecture, engineering, or platform roles within financial services.
• Hands-on expertise in Market Risk data domains including trades, positions, sensitivities, P&L, VaR, stress, FRTB, regulatory reporting.
• Experience designing and operating modern data platforms including lakehouse, distributed compute, semantic layers.
• Production experience with Databricks, Spark, Delta Lake, plus working knowledge of SQL Server and SSIS to credibly lead migration.
• Strong leadership and collaboration with risk managers, quants, application owners, engineers, and governance.

Responsibilities

• Data Architecture: Oversee data architecture, including transactional and analytical stores, Lakehouse, semantic layers, and reporting models.
• Engineering Frameworks & Patterns: Develop reusable libraries for ingestion, quality, lineage, orchestration, errors, and recovery.
• Migration Leadership: Lead SSIS-to-Databricks migration, sequencing, parallel runs, cutover, and decommissioning.
• Risk Data Models: Design canonical data models for trades, positions, sensitivities, P&L, market data, VaR, stress, and regulatory reporting.
• Performance & Scale: Optimize SQL and Spark workloads to meet strict end-of-day SLAs.
• Data Observability: Integrate QA, SLA tracking, pipeline health, reconciliation, anomaly detection, and lineage.
• Full Lifecycle Ownership: Design through deployment and ongoing operations; production supports daily ops.
• Stakeholder Partnership: Align with risk, finance, front office, and governance teams.

Skills

Data architecture

Data engineering

Databricks

Spark

Data modeling

Data observability

ETL modernization

Leadership

Stakeholder collaboration

Tools

SQL Server

SSIS

Delta Lake

Job description

Our client, a major bank in New York City, is looking for Data Architecture & Engineering Lead - Market Risk Technology.

New York City Location.

This is hybrid commute, 5 days per 2 weeks in the office.

Salary range 200-240K, plus around 20% bonus, plus great benefits package.

Data Architecture & Engineering Lead — Market Risk Technology

As the leader of data architecture and engineering for bank's Market Risk technology platform, you will oversee the end-to-end design, deployment, and operations of critical data solutions supporting VaR, FRTB, sensitivities, stress testing, limits, and regulatory reporting.

Your accountability spans the platform’s architectural vision, engineering frameworks, and the operational outcomes delivered to Risk, Finance, and Front Office stakeholders.

The platform is currently undergoing a migration from SSIS/SQL Server to Databricks and Delta Lake. You will be responsible for setting the target architecture, driving this migration to completion, and ensuring the stability of the legacy estate until it is fully decommissioned.

Role:

• Data Architecture: Oversee the platform’s data architecture, including transactional and analytical stores, Lakehouse implementation, semantic layers, and reporting models. Establish scalable standards that apply across teams rather than bespoke designs for individual projects.
• Engineering Frameworks & Patterns: Develop and enforce reusable libraries and frameworks for data ingestion, quality, reconciliation, lineage, orchestration, error handling, and recovery. Ensure these are built once and adopted broadly across the organization.
• Migration Leadership: Lead the SSIS-to-Databricks migration program, including sequencing, parallel-run reconciliation, cutover, decommissioning, and operational handover at each step.
• Risk Data Models: Design canonical and semantically clear data models for trades, positions, sensitivities, P&L, market data, VaR, stress, limits, capital, and regulatory reporting. Ensure models are fit for the consumers who rely on them.
• Performance & Scale: Optimize SQL and Spark workloads, partitioning, storage design, and query performance to meet stringent EOD SLAs.
• Data Observability: Integrate quality assurance, SLA tracking, pipeline health, reconciliation, anomaly detection, and lineage as core platform capabilities, not afterthoughts.
• Full Lifecycle Ownership: Guide the platform from design through deployment and ongoing operations. While production support handles daily operations, you own the engineering discipline that underpins their success.
• Stakeholder Partnership: Collaborate closely with risk managers, quants, application owners, engineers, support teams, infrastructure, and governance to ensure alignment and platform excellence.

Required Experience

• 15+ years in data architecture, engineering, or platform roles within financial services.
• Hands-on expertise in Market Risk data domains including trades, positions, sensitivities, P&L, market data, VaR, stress, FRTB, capital, and regulatory reporting, with the depth to model the domain, not just describe it.
• Proven experience designing and operating modern data platforms including lakehouse, distributed compute, semantic layers, and hybrid on-prem/cloud environments.
• Production experience with Databricks, Spark, and Delta Lake, plus working knowledge of SQL Server and SSIS to credibly lead migration.
• Expertise in data modeling for operational, dimensional, semantic, and canonical models.
• Demonstrated leadership of large ETL modernization initiatives, including sequencing, parallel-run, cutover, and decommissioning.
• Track record of building reusable engineering frameworks adopted across teams.
• Experience in performance tuning at scale for SQL Server, Databricks, Spark, storage formats, and query optimization.
• Architectural leadership: setting direction, reviewing designs, challenging weak patterns, and mentoring engineers and architects.
• Strong communication skills, capable of defending architectural decisions to senior technology, risk, and business leaders.