Make your mark by leading a major modernization that replaces legacy reconciliations capabilities with a new, resilient platform at scale

As a Director of Software Engineering at JPMorgan Chase, within the Commercial and Investment Bank, at reconciliations platform you are accountable for the target-state architecture and delivery outcomes of a new reconciliations stack replacing multiple legacy capabilities. You define reference architectures, approved patterns, and measurable non-functional targets, and you drive consistent implementation across multiple engineering teams. You lead modernization execution from parallel run through cutover and decommission, with strong operational discipline in a controls-heavy domain. You set expectations for production readiness, performance engineering, and auditability to sustain reliability at extreme scale.

Job Responsibilities

Owns the target-state platform architecture and ensure consistent implementation through reference architectures, approved patterns, and reusable golden path guidance.
Defines measurable non-functional requirements and targets (for example throughput, latency, recovery objectives, correctness guarantees, and cost boundaries) and drive adoption across teams.
Decides and documents key engineering trade-offs (for example consistency versus latency, streaming versus batch, storage layout, replay strategy, partitioning and compaction approach, and disaster recovery posture) for governance and reuse.
Leads delivery outcomes across multiple workstreams, including real-time processing, batch processing, workflow and state, controls and reporting, and migration tooling.
Drives execution discipline through dependency management, milestone planning, risk reduction, capacity planning, and escalation of cross-organization blockers.
Owns the modernization and migration strategy, including parallel run, parity validation, acceptance criteria, phased cutovers, rollback plans, and legacy decommission sequencing.
Establishes production readiness standards across the software delivery lifecycle, including testing strategy, continuous integration and delivery expectations, observability, incident management, and change governance aligned to a controls-heavy environment.
Leads performance and cost engineering for Java services and large-scale data pipelines, ensuring capacity planning and optimization are proactive and measurable.
Ensures auditability and explainability through reproducible outputs, lineage expectations, immutable retention strategy, and controlled reprocessing approaches.
Coaches senior engineers and technical leads through design reviews, code review standards, post-incident learning, and stakeholder-facing architecture communication.

Required qualifications, capabilities and skills

Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
8+ years of technical leadership experience owning system design and delivery for complex platforms.
Demonstrated accountability for production outcomes (reliability, incident management, and operational discipline) for large-scale distributed systems.
Strong Amazon Web Services production experience, including Elastic MapReduce, Lambda, Elastic Container Service, and DynamoDB.
Proven expertise designing distributed systems with idempotency, ordering, retries, consistency controls, and replay or reprocessing strategies.
Experience delivering large-scale data processing solutions using lakehouse patterns and table formats.

Preferred qualifications, capabilities and skills

Hands-on experience with Apache Iceberg at scale (partitioning strategy, compaction, schema evolution, and maintenance automation).
Advanced Apache Spark tuning experience at very large scale (skew, shuffle optimization, partition sizing, and cost-performance trade-offs).
Experience building reconciliations or matching engines within financial services.
Experience leading large migration programs that replace legacy platforms with parallel run and controlled cutover.