VP, Senior Developer - Liquidity Risk

This is a hands-on delivery role that explicitly combines Technical Business Analysis and Engineering/Development. You will own outcomes end-to-end: partner with Liquidity Risk and Treasury stakeholders to shape requirements and controls, then personally work in the data and code (SQL/Python/Unix) to build, validate, and release solutions into production.

What You’ll Deliver:

You will lead and implement technology initiatives that support Liquidity Risk Management capabilities such as cash positioning, funding and maturity profiles, collateral/margin impacts, liquidity metrics, stress testing/scenario outputs, limits/threshold monitoring, and internal/regulatory-oriented reporting, with strong data lineage, controls, and auditability.

Key Responsibilities:

End-to-End Delivery & Stakeholder Leadership

• Lead delivery of Liquidity Risk technology enhancements and new onboarding/integration initiatives across Liquidity Risk, Treasury/Funding, Finance, Operations, and IT. Own the full lifecycle: requirements discovery data mapping/lineage solution design build execution testing/UAT release planning- post-implementation validation.
• Serve as the primary point of contact for scope, priorities, dependencies, and issue resolution; communicate status and risks clearly to senior stakeholders.
• Contribute to delivery standards/best practices; mentor junior team members as needed.

Hands-On Engineering: Data, Code, and Production Readiness

• Develop and optimize advanced SQL to support liquidity metric calculations, exposure/aggregation views, reconciliations, controls, and drill-down diagnostics (joins, CTEs, window functions; performance tuning concepts).
• Build Python tooling for data transformation, automation, monitoring/diagnostics, and analytical workflows (pandas/batch patterns preferred).
• Use Unix/Linux and shell scripting to support batch processes, operational tooling, and production support diagnostics.
• Perform root-cause analysis for data/metric/reporting breaks (mappings, identifiers, cutoffs/timing, model assumptions/inputs) and drive remediation to closure.

Liquidity Data Enablement: Cash, Funding, Collateral & Flows

• Analyze and validate core liquidity inputs including cash balances, settlement/payment flows, secured/unsecured funding, collateral movements, margin, and maturity profiles.
• Define and implement mappings and controls from upstream sources (e.g., Treasury platforms, GL/subledger, payments, collateral/margin systems, market/security master where needed) to downstream consumers (liquidity risk engines, stress pipelines, dashboards, internal reporting).
• Build reconciliations and exception monitoring for cash positions, funding balances, and flow projections, including completeness checks and end-of-day cutoff logic.

Liquidity Metrics, Stress Testing & Reporting Traceability

• Enhance analytics pipelines for metrics such as liquidity buffers, survival horizon, cash flow gap/laddering, concentration, encumbrance, funding mix, and stress/scenario impacts.
• Support limit/threshold monitoring through drill-downs and roll forwards. Ensure strong traceability: source balances/flows transformations liquidity metrics- reports, with audit-ready evidence and reproducibility.

Documentation, Controls & SDLC Governance

• Produce and maintain high-quality artifacts: business/functional requirements, technical specs, solution designs, data dictionaries/mappings, test plans, validation evidence, and release notes.
• Manage user stories, defects, and delivery workflows in JIRA (Confluence preferred). Define acceptance criteria; partner with QA/UAT to validate results and support sign-off under strong change control.

Required Experience & Skills

• 7+ years (VP-level) or 5+ years (senior consultant) in a Developer/BA hybrid, Technical BA, Risk IT Analyst, or engineering role with significant delivery ownership in financial services.
• Demonstrated delivery into production systems involving risk/treasury/finance data and complex cross-team dependencies.

Technical:

• Advanced SQL on enterprise relational databases (e.g., Oracle/SQL Server/PostgreSQL): complex querying and tuning; strong reconciliation and data quality patterns.
• Python for automation and analysis; ability to build pragmatic tooling that improves reliability and speed of investigation.
• Unix/Linux command line and shell scripting; comfort supporting batch cycles, cutoffs, restarts, and operational di…