Job Description

Salary: $61,000 - 101,000 per year Requirements:

• We require a bachelors degree in Computer Science, Information Technology, or a closely related discipline.
• We need 5+ years of hands-on data engineering experience with deep AWS, data lake, and Snowflake expertise.
• We expect practical experience with modern data lake and data warehousing platforms such as Redshift, BigQuery, and Snowflake, along with processing engines like Spark, Flink, or Trino.
• We look for proficiency in SQL and familiarity with ETL and data pipeline tooling.
• We require experience applying Agile practices, including running ceremonies and managing backlogs for ongoing improvement.
• We need proven experience designing streaming pipelines with Kafka, Pub/Sub, or similar messaging systems.
• We expect experience with large-scale distributed data processing and performance tuning.
• We require the ability to design and deliver large-scale data solutions in cloud environments.
• We value demonstrated use of enterprise-authorized AI capabilities to support data engineering work, with strong validation habits and awareness of data sensitivity.
• We need the ability to review and verify AI-assisted outputs before use and escalate when uncertainty remains.
• Preferred: experience with data modeling in Erwin.
• Preferred: experience with table formats such as Iceberg and Hudi.

• We design, build, maintain, and tune scalable batch and streaming data pipelines with strong performance, resilience, and observability.
• We develop and run workflow orchestration to schedule, monitor, and manage data movement and transformation jobs.
• We translate complex business needs into technical solutions aligned with data lake and data warehousing standards.
• We use enterprise-authorized AI capabilities to accelerate pipeline design analysis and documentation, while validating outputs and handling data securely.
• We apply reuse-first, AI-assisted practices to improve SDLC-quality routines such as test generation and control validation, ensuring traceability, auditability, resiliency, and security.
• We establish and maintain governance processes for data modeling, cataloging, ownership, and access control.
• We mentor and train team members on best practices for data publication and lead technical direction through standards, reviews, and knowledge sharing.
• We stay current with advances in AWS Data Lake, Snowflake Data Warehouse, and related technologies.
• We perform advanced quantitative analysis on large datasets to uncover business trends.
• We manage data sharing, exchange, and ecosystem-specific capabilities.

• AI
• AWS
• Redshift
• BigQuery
• Cloud
• Data Warehouse
• ETL
• Flink
• Support
• Kafka
• SQL
• Security
• Snowflake
• Spark
• Mobile

More:

We are working with hackajob to connect exceptional professionals with J.P. Morgan for this opportunity. You will join our Consumer and Community Banking team as a Lead Data Engineering professional, helping us design secure, scalable, and efficient data solutions that support business goals and regulatory needs. Our group serves consumers, small businesses, municipalities, and nonprofits, delivering innovative banking, lending, payments, mortgage, credit, and digital experiences. We offer a competitive total rewards package, with compensation tied to role, experience, skills, and location, along with a broad range of benefits for eligible employees including health coverage, wellness resources, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. We are committed to diversity, inclusion, equal opportunity, and reasonable accommodations for applicants and employees.

last updated 39 week of 2026

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