Lead Data Engineer

Join JPMorgan Chase in the Consumer and Community Banking team as a Lead Data Engineer in Plano, Texas. This onsite role emphasizes designing and maintaining scalable data pipelines and architectures, elevating data governance and performance, and mentoring engineers to deliver secure, compliant data solutions. You will contribute to a culture that values reliability, security, and meaningful impact through advanced analytics and robust data platforms.

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

• Design and implement 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 that align with data lake and data warehouse standards.
• Leverage enterprise-authorized AI capabilities to accelerate data pipeline design and analysis, document outputs, and ensure data handling meets sensitivity and security requirements.
• Apply reuse-first, AI-assisted practices to strengthen SDLC quality routines for data pipelines, including test generation and control validation, ensuring traceability and auditability aligned with 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, guiding the team 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.

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 (Redshift, BigQuery, Snowflake) and engines such as Spark, Flink, or Trino.
• Experience applying Agile methodologies, running ceremonies, and prioritizing backlogs for continuous improvement.
• Proficiency in SQL and experience with data pipeline/ETL tools.
• Demonstrated experience using 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, escalating when uncertain and adhering to data handling requirements.
• Experience designing and building streaming pipelines with 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