Lead Software Engineer - Data Engineer

JPMorganChase; Jersey City (NJ); On-site; USD 95,000 - 125,000; Full time

Benefits offered by this job

Comprehensive health care coverage

Retirement savings plan

Tuition reimbursement

Job summary

JPMorganChase is seeking a data engineering professional in Jersey City, NJ. The role involves developing high-quality code in Python and PySpark, executing innovative software solutions, and leading community practices.

The ideal candidate has 5+ years of experience in software engineering and data pipelines. The position offers a competitive salary along with a comprehensive benefits package including health care, retirement plans, and wellness programs.

Qualifications

• 5+ years of applied experience building production data engineering and/or software engineering solutions.
• Advanced proficiency in Python and strong hands‑on experience with PySpark.
• Demonstrated experience leading effective use of approved AI‑assisted software development tools.

Responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting.
• Develops secure high-quality production code in Python/PySpark.
• Leads communities of practice across Software Engineering to drive awareness of new technologies.

Skills

Python

PySpark

Spark SQL

AI-assisted engineering

Data pipeline reliability

AWS data management

Education

Formal training or certification in software engineering

5+ years of applied experience in data engineering

Tools

AWS Glue

Apache Iceberg

Job description

Job Description

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

Job Responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems, with a focus on data engineering and Spark-based ETL/ELT
• Develops secure high-quality production code in Python/PySpark and Spark SQL, and reviews and debugs code written by others (Spark jobs, SQL logic, and data issues end-to-end)
• 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.
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems, including data pipeline reliability and lakehouse maintenance automation
• 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 (e.g., EMR/Databricks, lakehouse/table formats, catalog/governance patterns)
• Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies, especially around Spark performance, Iceberg best practices, and data platform operations
• Adds to team culture of diversity, opportunity, inclusion, and respect

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 5+ years of applied experience building production data engineering and/or software engineering solutions (design, development, testing, operations)
• Hands‑on practical experience delivering system design, application development, testing, and operational stability for large‑scale data pipelines
• Advanced in one or more programming language(s), with advanced proficiency in Python and strong hands‑on experience with PySpark.
• Advanced proficiency in Spark SQL and strong SQL fundamentals (data modeling, query optimization, execution plan analysis)
• 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 practice
• Experience with AWS data management patterns including S3 and AWS Glue Data Catalog (metadata governance, table schema hygiene, discoverability). Would also consider other cloud based Data platform.
• Required platform experience: delivering and operating Spark workloads on EMR and or Databricks (tuning, troubleshooting, monitoring, and cost, performance optimization)
• Required lakehouse expertise: production experience with Apache Iceberg, including table design and ongoing operations such as partitioning strategy and file layout optimization, schema evolution and compatibility controls, compaction, small-file mitigation, snapshot retention management and metadata maintenance, safe backfills and rewrites, reprocessing patterns
• Proficiency in automation and continuous delivery methods (CI/CD, automated testing, and repeatable deployments for data pipelines)

Preferred Qualifications, Capabilities, and Skills

• Kafka familiarity (topic design, producer/consumer patterns, schema evolution/compatibility, and operational considerations) is a plus
• Experience with Delta Lake concepts and trade-offs vs. Iceberg
• Experience with Spark Structured Streaming and streaming ETL patterns
• Working knowledge of Java (interoperability or leveraging existing JVM-based components)
• Experience using AI‑assisted engineering tools and workflows (e.g., GitHub Copilot, Claude) including spec-driven development, prompt-assisted refactoring, and code review—following enterprise-safe usage patterns

Compensation & Benefits

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission‑based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on‑site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

Equal Opportunity Employment

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans