Sr Lead Software Engineer Finance Technology TCIO Risk

Description for Internal Candidates
When you mentor and advise multiple technical teams and move financial technologies forward its 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 (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).