Lead Software Engineer, Data Governance Engineer Lead

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Plano, TX
Posted: 45 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 $204k

$180k most similar roles pay here $230k

This listing doesn't post a salary. Most similar roles pay $192,050–$216,000.

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 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, Data Governance Engineer Lead

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Lead Software Engineer - Data Governance Engineer Lead joins the Corporate Technology team as a core technical contributor within an agile environment. This role focuses on building and maintaining end-to-end data governance solutions, including enterprise data models, metadata standards, and governed ETL/ELT pipelines to operationalize corporate policies. The engineer will implement data cataloging, lineage tracking, and quality controls while driving the adoption of authorized AI-assisted engineering practices for code review, refactoring, and test acceleration. Key technical requirements include expert proficiency in Databricks with Delta Lake and Unity Catalog, Snowflake for warehouse optimization, AWS S3 for data lake implementations, and Teradata query management. The role requires advanced skills in conceptual, logical, and physical data modeling using tools like Erwin or PowerDesigner to solve complex problems regarding data consistency, security, and automated quality monitoring.

Skills

Databricks Delta Lake Unity Catalog Snowflake AWS S3 Teradata ETL ELT Data Modeling SQL AI-assisted Engineering Data Governance Data Lineage Erwin PowerDesigner Distributed Processing

What you'll do

Implement and maintain end-to-end data governance solutions based on enterprise standards and policies.
Drive team adoption of AI-assisted engineering practices to improve code quality and delivery speed.
Create and maintain conceptual, logical, and physical enterprise data models for business processes.
Define and maintain metadata standards including business glossaries and data dictionaries.
Implement data cataloging capabilities and track data lineage from source to consumption.
Build and maintain governed ETL/ELT pipelines that align with governance requirements.
Implement technical data quality controls, including profiling, rule definition, and monitoring workflows.
Manage large-scale data environments using Databricks, Snowflake, and AWS S3 infrastructure.

What we're looking for

Expert proficiency in data engineering fundamentals including ETL/ELT development, integration patterns, and distributed processing.
Demonstrated experience leading the use of approved AI-assisted software development tools while ensuring security and performance standards.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling of inputs/outputs.
Strong knowledge of data architecture and modeling patterns, including dimensional modeling and database design.
Advanced experience with Databricks, specifically Delta Lake, Unity Catalog, and Databricks SQL.
Demonstrated experience with Snowflake, including virtual warehouse optimization, data sharing, and platform security features.
Proficiency with AWS, particularly S3 for data lake implementations.
Strong working knowledge of Teradata, including query optimization and migration to cloud platforms.
Expert-level data modeling skills using industry-standard methodologies and tools like Erwin or PowerDesigner.
Ability to design transactional and analytical models aligned to business requirements.
Advanced ability to profile data and implement quality rules and monitoring frameworks (preferred).
Experience implementing data quality capabilities for accuracy, completeness, consistency, and timeliness (preferred).

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