Data Engineer III - Data Engineering, Databricks, Model Serving, ML Ops, Data Architecture and modeling

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

As a Data Engineer III at JPMorganChase within the JPM - US Wealth Management Tech, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm's business objectives.

Job responsibilities

• Architects and maintains large-scale data pipelines using Apache Spark and related distributed computing frameworks, ensuring efficient processing of structured and unstructured data.
• Builds and optimizes both batch and streaming data workloads to support analytical and operational use cases.
• Develops, optimizes, and productionizes notebooks, jobs, and workflows within the Databricks environment to support analytical and machine learning use cases.
• Designs and implements data architecture and data modeling strategies, including dimensional models, lakehouse structures, and schema design that balance performance, scalability, and governance requirements.
• Writes and optimizes complex SQL queries for data transformation, validation, and performance tuning across large datasets.
• Supports ML model serving infrastructure, ensuring trained models are deployed, monitored, and scaled reliably in production environments.
• Collaborates closely with data scientists and ML engineers on MLOps practices, including model versioning, CI/CD pipelines for ML artifacts, monitoring, and automated retraining workflows.
• Partners with cross-functional stakeholders - including analytics, risk, and platform engineering teams - to translate business requirements into technical data solutions.
• Maintains strong data quality, lineage, and security standards consistent with data governance and compliance expectations.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

• Formal training or certification on data engineering concepts and 3+ years applied experience.
• Demonstrates strong, hands-on proficiency with Apache Spark or comparable distributed compute frameworks, including experience optimizing jobs for performance and cost at scale.
• Holds direct, production-level experience with Databricks, including notebooks, Delta Lake, job orchestration, and cluster management.
• Demonstrates solid command of SQL, with the ability to write, debug, and optimize complex queries against large and varied datasets.
• Brings practical experience supporting ML model serving infrastructure in production environments.
• Shows familiarity with MLOps tooling and practices, such as model monitoring, versioning, and deployment automation.
• Possesses a strong foundation in data architecture and data modeling, covering both operational and analytical data store design.
• Has working knowledge of cloud data platforms such as AWS, Azure, or GCP. Maintains proficiency with version control systems such as Git.
• Demonstrates hands-on exposure to distributed computing, cloud data platforms, and production ML infrastructure.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices

• Experience with workflow orchestration tools such as Airflow or Databricks Workflows.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Exposure to CI/CD pipelines applied to data and ML workflows. Familiarity with feature stores and model registries such as MLflow.
• Experience with real-time streaming architectures such as Kafka and Spark Structured Streaming.
• Prior experience in financial services or other highly regulated industries, with an understanding of data governance and compliance requirements.