Lead Software Engineer - Data Platform
Jersey City, NJ, United States
- Pay
- Pay not listedSimilar roles state $127K–$190K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and build enterprise data processing and ingestion apps with Spark, Kafka and Flink
Build event-driven microservices with Kafka, including retries and dead-letter queues
Ensure production readiness through observability, alerting and incident runbooks
Develop secure production code and review and debug others' code
Partner with product managers and business stakeholders to turn requirements into solutions
From the employer’s posting
Design, build and manage enterprise applications at scale to ensure robust and fault tolerant data processing and ingestion using Spark, Kafka, and Flink
Design and build event-driven microservices workflows, using Kafka for inter-service communication and reliable asynchronous processing (at-least-once delivery, retries, and DLQ patterns).
Ensure production readiness through observability (metrics, logs, tracing), alerting, incident response runbooks, and proactive problem management.
Develops secure high-quality production code, and reviews and debugs code written by others
Partners with product managers and business stakeholders to translate requirements into production-grade solutions.
Tools in this posting
- Java
- AWS
- Databricks
- Kafka
- Redshift
- S3
- Snowflake
- Spark
- Airflow
Source — Tool mentions in context
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices - Advanced and hands on experience with development using Java, including performance tuning and optimizations. - Hands-on experience with distributed data processing frameworks such as Apache Spark and Flink
- Solid understanding of data modeling techniques (star schema, snowflake) and query optimization - Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent) - Experience with AWS cloud platforms, including deploying and operating services in a cloud environment
- Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent) - Experience with AWS cloud platforms, including deploying and operating services in a cloud environment - Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills - Experience in designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow is a plus - Experience with data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption is a plus
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems - Design, build and manage enterprise applications at scale to ensure robust and fault tolerant data processing and ingestion using Spark, Kafka, and Flink - Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation. - Design and build event-driven microservices workflows, using Kafka for inter-service communication and reliable asynchronous processing (at-least-once delivery, retries, and DLQ patterns). - Partners with product managers and business stakeholders to translate requirements into production-grade solutions.
- Hands-on experience with distributed data processing frameworks such as Apache Spark and Flink - Solid understanding of data modeling techniques (star schema, snowflake) and query optimization - Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)
- Advanced and hands on experience with development using Java, including performance tuning and optimizations. - Hands-on experience with distributed data processing frameworks such as Apache Spark and Flink - Solid understanding of data modeling techniques (star schema, snowflake) and query optimization
Posting record
Report an errorActively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 27, 2026 · 1 day ago
- Recorded sightings
- 5
- Last seen by us
- Oct 28, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
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Job description
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer - Data Platform at JPMorgan Chase, within the Commercial & Investment Banking – Data Analytics – Payments Technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Design, build and manage enterprise applications at scale to ensure robust and fault tolerant data processing and ingestion using Spark, Kafka, and Flink
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Design and build event-driven microservices workflows, using Kafka for inter-service communication and reliable asynchronous processing (at-least-once delivery, retries, and DLQ patterns).
- Partners with product managers and business stakeholders to translate requirements into production-grade solutions.
- Ensure production readiness through observability (metrics, logs, tracing), alerting, incident response runbooks, and proactive problem management.
- Develops secure high-quality production code, and reviews and debugs code written by others
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years of applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Demonstrated professional experience focused on software engineering or data platform development
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Advanced and hands on experience with development using Java, including performance tuning and optimizations.
- Hands-on experience with distributed data processing frameworks such as Apache Spark and Flink
- Solid understanding of data modeling techniques (star schema, snowflake) and query optimization
- Proficiency with cloud data services (AWS S3, Glue, Redshift, Athena, EMR, Lake Formation, or equivalent)
- Experience with AWS cloud platforms, including deploying and operating services in a cloud environment
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
- Experience in designing and operating data pipelines on Databricks using orchestration tools such as Apache Airflow is a plus
- Experience with data modeling strategies (fact and dimensional, wide tables) to support analytics, reporting, and downstream consumption is a plus
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Source details
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- Pay
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- Location & working pattern
Jersey City, NJ, United States
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