Lead Software Engineer, Financial Services Data Engineering

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Jersey City, NJ
Employment: Full-time
Posted: 27 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $182k

$130k $235k

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This listing doesn't post a salary. Most similar roles pay $139,914–$225,000.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Financial Services Data Engineering

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As a Lead Software Engineer- Financial Services Data Engineering: Pyspark / Java / BigData / Datalake / AI, you will join the Asset and Wealth Management- Analytics & Applied AI Team. You will lead an AI-first transformation of data engineering by productizing the data product lifecycle, including semantic alignment, automated lineage, and data quality. Your daily responsibilities involve building human-in-the-loop autonomous agents to detect schema drift, triage data incidents, and generate governance evidence. You will also migrate legacy reporting stacks to a strategic mesh ecosystem using Databricks, Iceberg, and Snowflake. The role requires deep expertise in Python, PySpark, and Java to build robust pipelines and ML pipelines. You will solve complex business problems by industrializing entity resolution with LLMs and ensuring regulatory compliance across critical financial domains.

Skills

PySpark Java Python Databricks Spark Snowflake Iceberg Data Mesh LLM NLP ML Pipelines CI/CD Data Lake Lakehouse Agile Data Quality Software Development Life Cycle

What you'll do

Define and enforce the architecture for an AI-driven data product lifecycle including semantic alignment and automated lineage.
Build and operate autonomous agents to detect schema drift, propose transformations, and triage data incidents.
Lead the migration and modernization of legacy analytics stacks to the strategic data mesh ecosystem.
Design and develop real-time and batch analytics platforms capable of running complex reporting.
Industrialize entity resolution and parent identification using ML and LLM solutions.
Drive team adoption of AI-assisted engineering practices to improve code quality and delivery speed.
Embed governance, lineage, and data quality by design across critical domains and regulatory reporting.
Establish monitoring and alerting for solution performance, scalability, availability, and reliability.

What we're looking for

Formal training or certification on software engineering concepts and 5+ years of applied experience.
Proven leadership delivering AI-first analytics and data engineering at scale, including productized data mesh patterns and semantic layers.
Experience developing data ingestion and integration processes from multiple platforms with data cleansing and transformation rules.
Deep hands-on experience with big data and modern data platforms like Spark, Databricks, Snowflake, and Iceberg.
Strong programming capability in Python and PySpark, or Java, with strong CI/CD and containerization practices.
Applied AI expertise in ML pipelines, NLP/LLMs, and agentic frameworks to build autonomous agents for engineering tasks.
Governance proficiency across lineage, data quality, and access control aligned to regulatory expectations.
Proficiency in all aspects of the Software Development Life Cycle.
Python and Java (preferred).