Data Architecture Market Risk Lead

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.

• 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.
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