Lead Data Engineer

Lead Data Engineer at JPMorgan Chase in Plano, TX onsite within the Consumer and Community Banking team, responsible for designing, developing, and maintaining scalable data pipelines and architectures, advancing data governance and performance optimization, and mentoring engineers to deliver secure, compliant data solutions.

Responsibilities

• Architect and implement scalable batch and streaming data pipelines with strong performance, resilience, and observability.
• Design and operate workflow orchestration to schedule, monitor, and govern data movement and transformations.
• Translate business requirements into compliant data lake and data warehouse solutions.
• Leverage enterprise AI capabilities to accelerate pipeline design analysis and documentation, validating results and enforcing data sensitivity and security practices.
• Adopt reuse-first, AI-assisted SDLC practices to strengthen data pipeline quality routines, including test generation and control validation, with traceability and compliance to resiliency and security standards.
• Establish and maintain governance 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.
• Conduct advanced quantitative analyses on large datasets to identify business trends and insights.
• Manage data sharing, exchanges, and ecosystem-specific features.

Requirements

• Bachelor's degree in Computer Science, Information Technology, or a related field.
• 5+ years of data engineering experience with deep AWS, Data Lake, and Snowflake expertise.
• Hands-on experience with modern data lake and data warehousing technologies such as Redshift, BigQuery, Snowflake, and engines like Spark, Flink, or Trino.
• Experience applying Agile methodologies, leading ceremonies, and prioritizing backlogs for continuous improvement.
• Strong SQL proficiency and experience with data pipeline and ETL tools.
• Experience using enterprise-authorized AI capabilities to support data engineering workflows with solid validation practices and awareness of data sensitivity.
• Ability to review and validate AI-assisted outputs 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.
• Experience designing and implementing large-scale cloud-based data solutions.

Technologies

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

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