Lead Data Engineer

JPMorgan Chase is hiring a Lead Data Engineer for the Consumer and Community Banking team in Plano, TX 75024 (onsite). In this role, you will design and deliver scalable data pipelines and architectures that support business and regulatory requirements, with a clear focus on data governance, performance, and collaboration. You will also work with enterprise-authorized AI capabilities to accelerate pipeline design and documentation, while maintaining strong validation and sensitivity-aware data handling.

Responsibilities

• Design, build, maintain, and optimize scalable batch and streaming data pipelines with performance, fault tolerance, and observability.
• Develop and operate workflow orchestration to schedule, monitor, and manage data movement and transformations.
• Translate complex business needs into technical solutions aligned to data lake and data warehousing standards.
• Use enterprise-authorized AI capabilities to accelerate data pipeline and design analysis, validating outputs and applying data handling rules based on sensitivity and security requirements.
• Adopt reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines, including test generation and control validation, with traceability 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, leading technical direction through standards, reviews, and knowledge sharing.
• Stay current with advancements across AWS Data Lake and Snowflake Data Warehouse and related technologies.
• Conduct advanced quantitative analysis on large datasets to identify business trends.
• Manage data sharing and exchange, including ecosystem-specific capabilities.

Requirements

• 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, such as Redshift, BigQuery, and Snowflake, plus engines including Spark, Flink, or Trino.
• Apply Agile methodologies, running ceremonies and prioritizing backlogs for continuous improvement.
• Proficiency in SQL and experience with data pipeline/ETL tools.
• Experience using enterprise-authorized AI 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 (for example, 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.
• Ability to design and implement large-scale data solutions in cloud environments.

Technology Focus

AWS Data Lake, Snowflake Data Warehouse, Redshift, BigQuery, Snowflake, Spark, Flink, Trino, SQL, Kafka, Pub/Sub, Erwin, Iceberg, Hudi.

Preferred Qualifications

• Experience with data modeling in Erwin.
• Experience with table formats such as Iceberg and Hudi.