Lead Data Engineer

Talentify; Plano (TX); On-site; USD 130,000 - 170,000; Full time

Job summary

JPMorgan Chase in Plano, TX is seeking a Lead Data Engineer to design, develop, and maintain scalable data pipelines and architectures that support business objectives and regulatory requirements. You will drive data governance, performance optimization, and mentor engineers while collaborating with business, analytics and technology stakeholders.

The role emphasizes building data solutions in AWS Data Lake and Snowflake environments, with strong focus on SQL, streaming pipelines, and

Qualifications

• 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 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.

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.
• Use 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.
• Apply reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines, ensuring 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 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.

Skills

SQL

Data pipelines

AI-assisted workflows

Agile methodologies

Education

Bachelor's degree in Computer Science or related field

Tools

AWS

Data Lake

Snowflake

Redshift

BigQuery

Spark

Flink

Trino

Kafka

Pub/Sub

Erwin

Iceberg

Hudi

Job description

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