Sr Lead Software Engineer - Data Engineering

Embrace this pivotal role as an essential member of a high-performing team dedicated to reaching new heights in data engineering. Your contributions will be instrumental in shaping the future of one of the world's largest and most influential companies.
As a Senior Lead Software Engineer at JPMorganChase within Consumer & Community Banking Risk Technology, you are an integral part of an agile team that designs, builds, and delivers the data utilities and platforms our modeling, analytics, and risk partners depend on. This is a hands-on engineering role — you lead through technical depth, design authority, and the code you write. You will lead a Scrum team building cutting-edge data utilities, setting the technical direction and raising the engineering bar for everyone around you. Leverage your deep technical expertise and problem-solving capabilities to drive significant business impact and tackle a diverse array of challenges spanning real-time and batch data platforms, multiple data architectures, and a wide range of data consumers.
Job responsibilities
Design, build, and deliver reusable frameworks and platforms that support both high-throughput real-time applications and large-scale batch processing — including Spark workloads over datasets spanning billions of records — in a secure, stable, and scalable way
Write production code daily as the senior technical contributor on the team; lead by example on design quality, testing, and operational readiness
Lead a Scrum team building cutting-edge data utilities, setting technical direction, decomposing complex problems into deliverable work, and unblocking engineers without holding a reporting line
Build and develop the team's technical capability — mentoring engineers, raising code-review standards, growing depth in the platform stack, and creating the conditions for a self-reliant, high-performing team
Provide recommendations and insight on data management and governance procedures applicable to the acquisition, maintenance, validation, and utilization of data — spanning Data Governance, Data Quality, Data Profiling, and Data Lineage
Use the latest enterprise-authorized generative AI and agentic AI capabilities to accelerate design, implementation, testing, and technical documentation — validating outputs and handling data according to sensitivity and security requirements
Design and deliver trusted data collection, storage, access, and analytics platform solutions across AWS, Databricks, and Snowflake
Build streaming and low-latency serving components using Kafka, Cassandra, and related AWS services to support real-time consumption
Partner closely with product, architecture, modeling, and model-deployment teams to understand data requirements and deliver the engineering that feeds and serves them
Apply reuse-first, AI-assisted practices within delivery and operational routines — ensuring traceability, auditability, and alignment to resiliency and security expectations
Present technical designs, platform metrics, and data insights to engineering and business stakeholders through clear visual delivery
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Strong hands-on programming expertise in Java, Python, and Scala
Demonstrated experience building frameworks and platforms — not just applications — that other teams adopt and build on
Proven experience delivering both high-throughput real-time systems and high-volume Spark processing over very large datasets, with a working command of performance tuning at scale
Deep hands-on experience with AWS (compute, serverless, managed data services, IAM) and Databricks — including Unity Catalog, Delta Lake, Lakeflow, Delta Live Tables, Catalog Sharing, fine-grained access control, and SQL Warehouses
Hands-on experience with Snowflake in a production data platform environment
Working experience with Kafka, Cassandra, and related AWS streaming and NoSQL offerings
Strong understanding of both relational and NoSQL databases, including data modeling trade-offs between them
Demonstrated experience using enterprise-authorized generative AI capabilities to accelerate engineering delivery, with strong validation habits and awareness of data sensitivity
Experience working on AI/ML systems, with a solid understanding of core model families — decision trees, ensemble methods, and neural networks — and a conceptual understanding of transformer models
Mastery of Agile/Scrum delivery in practice
Preferred qualifications, capabilities, and skills
Experience mentoring and growing engineers and building team technical capability without formal management authority
AWS, Databricks, and/or Snowflake certifications
Strong grasp of Data Governance, Data Quality, Data Profiling, and Data Lineage in regulated environments
Hands-on experience with feature provisioning for ML — including Feature Store on Unity Catalog
Experience adopting agentic AI development tooling on a team and measurably accelerating deliver
Exposure to cloud migration or platform modernization at scale