Lead Data Engineer

JPMorganChase offers a comprehensive benefits package and on-site wellness resources for this Plano, Texas based role. As a Lead Data Engineer on the Consumer and Community Banking team, you will design scalable data pipelines and architectures, drive data governance and performance optimization, and mentor engineers to deliver secure, compliant data solutions.

Benefits

• Base salary
• Commission-based pay
• Discretionary incentive compensation (cash)
• Forfeitable equity
• Health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching

Responsibilities

• Architect and implement scalable batch and streaming pipelines with strong performance, fault tolerance, and observability
• Develop and manage workflow orchestration to schedule, monitor, and control data movement and transformations
• Translate complex business requirements into technical designs that align with data lake and data warehousing standards
• Utilize enterprise AI capabilities to speed up data pipeline design analysis and documentation, validating outputs and ensuring data handling aligns with sensitivity and security policies
• Adopt reuse-first, AI-assisted practices to strengthen SDLC quality routines for data pipelines, including test generation and control validation, ensuring traceability and compliance with resiliency and security expectations
• Build and sustain governance processes for data modeling, cataloging, ownership, and access control
• Mentor teammates on data publication best practices and guide the team's technical direction through standards, reviews, and knowledge sharing
• Stay current with advancements in AWS Data Lake, Snowflake Data Warehouse, and related technologies
• Perform advanced quantitative analysis of large datasets to identify business trends
• Manage data sharing, exchange, and ecosystem-specific features

Requirements

• Bachelor's degree in Computer Science, Information Technology, or a related field
• 5+ years of experience in data engineering with deep AWS, data lake, and Snowflake expertise
• Hands-on experience with modern data lake and warehousing technologies (eg, Redshift, BigQuery, Snowflake) and processing engines such as Spark, Flink, or Trino
• Experience applying Agile methodologies, leading ceremonies, and prioritizing backlogs for continuous improvement
• Proficiency in SQL and experience with data pipeline and ETL tools
• Demonstrated use of enterprise-authorized AI capabilities to support data engineering workflows with strong validation habits and awareness of data sensitivity
• Ability to review and validate AI-assisted outputs before use, escalate when uncertain, and follow data handling requirements
• Experience building streaming pipelines using Kafka, Pub/Sub, or similar messaging systems
• Experience with large-scale distributed data processing and performance tuning
• Design and implement large-scale data solutions in cloud environments

Technologies

• AWS
• Snowflake
• Redshift
• BigQuery
• Spark
• Flink
• Trino
• Kafka
• Pub/Sub
• SQL