Lead Software Engineer, Financial Services Data Engineering

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Posted: 46 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $195k

$172k most similar roles pay here $231k

This listing doesn't post a salary. Most similar roles pay $177,900–$212,875.

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 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

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

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Lead Software Engineer- Financial Services Data Engineering: Pyspark / Java / BigData / Datalake / AI joins the Asset and Wealth Management- Global Prime Brokerage Team to drive an AI-first transformation of data engineering. This role involves building human-in-the-loop autonomous agents to detect schema drift, triage incidents, and automate governance evidence while modernizing legacy analytics stacks into a strategic data mesh ecosystem. You will define the architecture for an AI-driven data product lifecycle including semantic alignment, automated lineage, and data quality. The position requires expertise in Python, PySpark, Java, Spark, Databricks, Snowflake, and Iceberg to build robust pipelines and lakehouse frameworks. Key responsibilities include industrializing entity resolution using ML/LLM solutions, implementing CI/CD practices, and ensuring regulatory compliance through automated monitoring. You will provide technical leadership to develop scalable, secure analytics platforms for complex financial data products.

Skills

PySpark Java Python Databricks Spark Snowflake Iceberg Data Lake Data Mesh LLM NLP ML Pipelines CI/CD Agile Data Quality

What you'll do

Define and enforce an AI-driven data product lifecycle including semantic alignment, automated lineage, and data quality.
Build human-in-the-loop autonomous agents to detect schema drift, triage incidents, and generate governance evidence.
Lead the migration of legacy analytics and reporting stacks to a strategic data mesh ecosystem.
Develop real-time and batch analytics solutions on modern platforms like Databricks, Spark, and Snowflake.
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.
Design and implement robust data ingestion pipelines and transformation rules for analytics-ready datasets.

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 cleansing and transformation rules.
Deep hands-on experience with big data and modern platforms like Spark, Databricks, Snowflake, and Iceberg.
Strong programming capability in Python, PySpark, or Java with 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.
Demonstrated experience leading the use of approved AI-assisted software development tools while ensuring security and performance.

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