Lead Data Engineer (Infrastructure Data Platforms)

JPMorgan Chase & Co.; Columbus (OH); Vor Ort; USD 61.000 - 101.000; Vollzeit

Zusammenfassung

JPMorgan Chase & Co. in Columbus, OH, is hiring a Lead Data Engineer (Forward Deployed) to collaborate with infrastructure product teams, design data contracts, and publish governed data products into a data mesh.

The role emphasizes data modeling, pipeline development, data quality, and interoperability across domains, with hands-on work in Python, SQL, Hadoop, Spark, Kafka, and cloud environments.

Qualifikationen

• Formal training or certification in data engineering concepts, with at least 5 years of applied experience
• At least 5 years of hands-on data engineering experience in data modeling, pipeline development, and data quality engineering
• Proficiency in Python and SQL for building reliable, testable data transformations and integrations
• Experience diagnosing data quality issues, including completeness, accuracy, timeliness, and consistency, and implementing controls to prevent recurrence
• Experience working directly with partner teams
• Working knowledge of infrastructure or asset data domains, such as compute, network, storage, or cloud, sufficient to model and normalize inventory data
• Strong problem-solving skills, including investigating complex data discrepancies across systems and dependencies
• Ability to navigate ambiguity and a fast-changing environment, lead initiatives, and drive work to completion
• Familiarity with data contract patterns and practical data quality frameworks, including rule definition, monitoring, and exception management
• Preferred: Experience with IT asset management or configuration management concepts, such as asset inventories or configuration management databases
• Preferred: Exposure to data mesh and data product operating models, including publishing reusable datasets
• Preferred: Experience with semantic modeling across infrastructure layers, connecting assets across application, platform, storage, and network contexts
• Preferred: Familiarity with graph databases or dependency mapping concepts
• Preferred: Experience with streaming services such as Kafka
• Preferred: Familiarity with anomaly detection, pattern analysis, or big data frameworks such as Hadoop or Spark
• Preferred: Working knowledge of Java to contribute to or improve existing Java-based platforms, such as Verum SOR

Aufgaben

• Embed with infrastructure product teams to identify current data sources, ownership, definitions, formats, and quality gaps, then develop a measurable enablement plan
• Design and deliver integrations that publish governed data products into a data mesh ecosystem, ensuring completeness, standardization, and lineage
• Establish source-level data quality rules and monitoring, lead remediation, and prevent recurring issues through root-cause analysis and lasting fixes
• Standardize critical data attributes and definitions across domains to support reliable downstream use, interoperability, and policy enforcement
• Define and implement data contracts that clarify producer and consumer expectations and reduce operational risk for dependent teams
• Reconcile and certify infrastructure asset inventories to address completeness and accuracy gaps that could create security and control exposure
• Work with product, engineering, and governance stakeholders to align on authoritative sources, stewardship, and decision rights for key infrastructure datasets
• Maintain metadata management practices, including cataloging, lineage, and stewardship signals, to support auditability and operational transparency

Kenntnisse

Data engineering

Python

SQL

Big Data

Kafka

Spark

Java

Cloud

Tools

Hadoop

Kafka

Spark

Java

SQL

Python

Jobbeschreibung

Salary: $61,000 - 101,000 per year Requirements:

• Formal training or certification in data engineering concepts, with at least 5 years of applied experience
• At least 5 years of hands-on data engineering experience in data modeling, pipeline development, and data quality engineering
• Proficiency in Python and SQL for building reliable, testable data transformations and integrations
• Experience diagnosing data quality issues, including completeness, accuracy, timeliness, and consistency, and implementing controls to prevent recurrence
• Experience working directly with partner teams
• Working knowledge of infrastructure or asset data domains, such as compute, network, storage, or cloud, sufficient to model and normalize inventory data
• Strong problem-solving skills, including investigating complex data discrepancies across systems and dependencies
• Ability to navigate ambiguity and a fast-changing environment, lead initiatives, and drive work to completion
• Familiarity with data contract patterns and practical data quality frameworks, including rule definition, monitoring, and exception management
• Preferred: Experience with IT asset management or configuration management concepts, such as asset inventories or configuration management databases
• Preferred: Exposure to data mesh and data product operating models, including publishing reusable datasets
• Preferred: Experience with semantic modeling across infrastructure layers, connecting assets across application, platform, storage, and network contexts
• Preferred: Familiarity with graph databases or dependency mapping concepts
• Preferred: Experience with streaming services such as Kafka
• Preferred: Familiarity with anomaly detection, pattern analysis, or big data frameworks such as Hadoop or Spark
• Preferred: Working knowledge of Java to contribute to or improve existing Java-based platforms, such as Verum SOR

Responsibilities:

• Embed with infrastructure product teams to identify current data sources, ownership, definitions, formats, and quality gaps, then develop a measurable enablement plan
• Design and deliver integrations that publish governed data products into a data mesh ecosystem, ensuring completeness, standardization, and lineage
• Establish source-level data quality rules and monitoring, lead remediation, and prevent recurring issues through root-cause analysis and lasting fixes
• Standardize critical data attributes and definitions across domains to support reliable downstream use, interoperability, and policy enforcement
• Define and implement data contracts that clarify producer and consumer expectations and reduce operational risk for dependent teams
• Reconcile and certify infrastructure asset inventories to address completeness and accuracy gaps that could create security and control exposure
• Work with product, engineering, and governance stakeholders to align on authoritative sources, stewardship, and decision rights for key infrastructure datasets
• Maintain metadata management practices, including cataloging, lineage, and stewardship signals, to support auditability and operational transparency

Technologies:

• Big Data
• Cloud
• Hadoop
• Support
• Java
• Kafka
• Network
• Python
• SQL
• Security
• Spark
• DevOps

More:

We are JPMorganChase, a longstanding financial institution serving consumers, small businesses, and corporate, institutional, and government clients. In this Lead Data Engineer (Forward Deployed) role, you will join our Infrastructure Data Platforms team and partner with infrastructure product teams to create trusted, governed data products that support security controls and enterprise analytics. Our Corporate Technology organization develops applications and provides technology support across corporate functions, including Finance, Treasury, Risk Management, Human Resources, Compliance, and Legal. We offer competitive total rewards, with compensation based on role, experience, skills, and location, as well as eligible incentives and benefits such as health coverage, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. We value diversity and inclusion, are an equal opportunity employer, and provide reasonable accommodations for applicants and employees.

last updated 41 week of 2026