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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer III - **Company:** JPMorgan Chase & Co. - **Location:** Plano, TX, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Big Data, BigQuery, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Sharing, Data Systems, Data Warehousing, Distributed Computing Environment, Fault Tolerance, Enterprise Messaging Systems, Performance Tuning, Systems Development Life Cycle, SQL Databases, Data Streaming, Data Processing, Cloud Platform System, Snowflake, Apache Spark, Data Lakes, Information Technology, Apache Flink, AWS Data Analytics, Apache Kafka, Data Pipelines, Amazon Redshift - **Published:** July 17, 2026 - **Apply:** https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/requisitions/preview/210769357 ## About the Role * Hold a Bachelor's degree in Computer Science, Information Technology, or related field. * 3+ 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 ## Description As a Data Engineering III 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., * 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. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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