Lead Data Engineer

JPMorgan Chase & Co.
Columbus, OH, United States
7 days ago
Apply on www.jobmonkeyjobs.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours

Tech stack

Java (Programming Language) Big Data Configuration Management Databases Configuration Management Information Engineering Data Governance Data Transformation Graph Database Apache Hadoop Inventory Management Software Python (Programming Language) Meta-Data Management
+6 more
Pattern Recognition SQL Databases Apache Spark Apache Kafka Data Management Data Pipelines

Job description

As a Lead Data Engineer (Forward Deployed) at JPMorganChase within the Infrastructure Data Platforms team, you will embed with infrastructure product teams to turn how asset and configuration data exists today into trusted, governed data products that power security controls and enterprise analytics. You will partner directly with teams across compute, network, storage, and cloud to close data visibility gaps and strengthen the firm’s ability to detect and respond to emerging threats., * Embed with infrastructure product teams to discover current-state data sources, ownership, definitions, formats, and quality gaps, and translate findings into a measurable enablement plan

  • Design and deliver integrations that publish governed data products into a data mesh ecosystem, ensuring completeness, standardization, and lineage
  • Establish data quality rules and monitoring at the source, driving remediation and preventing recurring issues through root-cause analysis and durable fixes
  • Standardize critical data attributes and definitions across domains to enable reliable downstream consumption, interoperability, and policy enforcement
  • Define and implement data contracts that make producer/consumer expectations explicit and reduce operational risk for dependent teams
  • Reconcile and certify infrastructure asset inventories to close completeness and accuracy gaps that create security and control exposure
  • Partner with product, engineering, and governance stakeholders to align on authoritative sources, stewardship, and decision rights for key infrastructure datasets
  • Maintain strong metadata management practices (cataloging, lineage, and stewardship signals) to support auditability and operational transparency

Requirements

  • Formal training or certification on data engineering concepts and 5+ years applied experience
  • 5+ years of hands-on data engineering experience spanning data modeling, data pipeline development, and data quality engineering
  • Proficiency in Python and SQL, with the ability to build reliable, testable data transformations and integrations
  • Demonstrated experience diagnosing data quality issues (completeness, accuracy, timeliness, consistency) and implementing controls to prevent recurrence
  • Experience working directly with partner teams
  • Working knowledge of infrastructure or asset-related data domains (e.g., compute, network, storage, cloud) sufficient to model and normalize inventory data
  • Strong problem-solving skills, including the ability to investigate complex data discrepancies across multiple systems and dependencies
  • Comfortable dealing with ambiguity and a fast-changing environment, with the ability to lead and drive effort to completion
  • Familiarity with data contract patterns and practical data quality frameworks (rule definition, monitoring and exception management)

Preferred Qualifications, Capabilities, and Skills

  • Experience with IT asset management or configuration management concepts (e.g., asset inventories, configuration management databases)
  • Exposure to data mesh and data product operating models, including publishing reusable datasets for broad consumption
  • Experience with semantic modeling across infrastructure layers to connect assets across application, platform, storage, and network contexts
  • Familiarity with graph databases or dependency mapping concepts (e.g., using graph-style modeling to represent relationships between assets)
  • Experience with streaming services like Kafka
  • Familiarity with anomaly detection, pattern analysis, or big data frameworks such as Hadoop/Spark
  • Working knowledge of Java sufficient to contribute to or uplift existing Java-based platforms (e.g., Verum SOR) as needed

Benefits & conditions

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.

About the company

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.jobmonkeyjobs.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:34 min

Capabilities of the Apache Spark processing engine

Ayon Roy · LIVE

3:28 min

Defining big data and machine learning fundamentals

Ayon Roy · LIVE

5:43 min

Processing real-time event streams with Apache Kafka

Alex Soto Alex Soto · LIVE

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

2:10 min

Why organizations combine big data and machine learning

Ayon Roy · LIVE

3:09 min

Balancing data science skillings alongside systems engineering rigor

Nico Schmidt · LIVE

Videos

See all

Related articles

See all