Description for Internal Candidates

When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.

As a Senior Lead Software Engineer at JPMorgan Chase within Corporate Technology – Treasury & CIO (TCIO) team, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, and anticipate the needs and dependencies of Finance, Risk, Treasury, Quantitative Research, and Infrastructure.

Job responsibilities

• Provide overall direction, oversight, and coaching for teams of entry- to mid-level software engineers delivering solutions for Structural Interest Rate Risk (SIRR) and Asset-Liability Management (ALM) across banking book and investment portfolio.
• Provide accountability for decisions influencing resourcing, budget, tactical operations, and the execution of engineering processes and procedures across data, compute, and application layers.
• Ensure successful collaboration across stakeholders (Finance, Market Risk, Treasury, Quant, SRE, Data Platforms) to deliver secure, scalable, and well-controlled solutions.
• Identify and mitigate issues to execute the book of work; proactively escalate risks and drive remediation plans; enforce model input/output contracts and reproducibility for quant integrations.
• Provide input to leadership regarding budget, approach, and technical considerations (cloud adoption, data platform strategy, performance, resilience) to improve operational efficiencies and functionality.
• Create a culture of diversity, inclusion, mentorship, and thought leadership; set engineering standards (design docs, code reviews, testing, observability) and prioritize diverse representation.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Extensive software engineering experience and leading teams of technologists delivering production systems at scale.
• Experience leading multiple teams; ability to guide and coach on achieving goals aligned to strategic initiatives and regulatory timelines.
• Proven track record hiring, developing, mentoring, and recognizing engineering talent; builds clear technical ladders and career paths.
• Strong domain expertise in SIRR and ALM: DV01, BPV, duration/convexity, yield curve construction, EVE vs EaR, FTP/base rate curves, NII attribution; banking book vs investment portfolio nuances (NMDs, behavioral models, prepayment, securities, hedging).
• Stress testing and scenario design: parallel shifts, steepeners/flatteners, basis risk, idiosyncratic shocks; translating financial requirements into technical roadmaps.
• Quant model integration competence: interface with models on shared compute platforms; define/enforce model I/O contracts, versioning, reproducibility; orchestrate batch/near-real-time runs; partner with quants on calibration, validation, back testing.
• Scalable architecture and coding: AWS (S3, IAM, Lambda, ECS/EKS, Step Functions, CloudWatch), Databricks/Spark (Py Spark/Scala, Delta Lake, Unity Catalog, performance tuning), Python and Java (Spring Boot microservices, RESTful APIs), eventing/streaming (Kafka), workflow orchestration (Airflow/Step Functions); design for reusability (libraries, SDKs, shared services), backward-compatible APIs/versioning.
• Data engineering for risk platforms: time-series/panel data models, schema evolution, late-arriving data handling, idempotent processing; data quality controls (validations, reconciliations, lineage, audit trails), golden-source alignment; performance/reliability (SLAs, retries/backoff, checkpoints, state management).
• Practical cloud-native experience and expertise across core technology disciplines; degree in Computer Science, Engineering, Mathematics, or related field.

Preferred qualifications, capabilities, and skills

• Experience producing high-quality code and design at a senior level; sets standards, reviews designs, and drives technical direction.
• AI/ML enablement for anomaly detection, forecasting (NII/liquidity), and data quality signals; MLOps (feature stores, model registries, CI/CD for models, drift monitoring, explainability).
• Controls, compliance, and operability: change management, segregation of duties, SOX-ready evidence, production runbooks; back testing frameworks/challenger models; observability (logging/tracing/metrics), incident response, and postmortems.
• Delivery mindset: de-risking with phased approaches, feature toggles, robust test environments; measures outcomes (latency, throughput, cost per run, data quality KPIs, incident reduction).