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 experienceExtensive 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 (PySpark/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).