Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Lead Data Engineering at JPMorgan Chase within the Consumer and Community Banking team, you design, develop, and maintain robust data pipelines and architectures.. You drive data governance, performance optimization, and mentor engineers while collaborating with business, analytics and technology stakeholders. You ensure scalable, secure, and efficient data solutions that support business objectives and regulatory requirements.
Job Responsibilities
- Design, build, maintain and optimize scalable batch and streaming data pipelines with strong performance, fault tolerance, and observability
- Develop and operate workflow orchestration to schedule, monitor, and manage data movement and transformations
- Translate complex business requirements into technical solutions meeting data lake and data warehousing standards.
- Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
- Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations
- Build and maintain governance processes for data modeling, cataloging, ownership, and access control.
- Provide mentorship and training on data publication best practices to team members and lead the team's technical direction through standards, reviews, and knowledge sharing
- Stay up-to-date 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.
Required Qualifications, Capabilities and Skills
- Hold a Bachelor’s degree in Computer Science, Information Technology, or 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 (e.g., Redshift, BigQuery, Snowflake; and engines such as Spark, Flink , or Trino.
- Apply Agile methodologies, running ceremonies and prioritizing backlogs for continuous improvement.
- Exhibit proficiency in SQL and experience with data pipeline/ETL tools.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
- Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.
- Experience designing and 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.
Preferred qualifications, capabilities, and skills
- Experience with data modeling in Erwin .
- Experience with table formats such as Iceberg, Hudi