Software Engineer - Data Engineer (Risk)

Salary: $42,500 - 70,500 per year Requirements:

• Formal training or certification in software engineering, computer science, data engineering, or a related field, plus at least 3 years of relevant industry experience.
• Strong practical experience with Databricks, Apache Spark, PySpark, Delta Lake, and Lakehouse architectures.
• Experience building and supporting large-scale batch and streaming data pipelines.
• Proficiency in Python and SQL, with a solid understanding of distributed computing.
• Hands-on use of enterprise-authorized AI-assisted development tools for activities such as coding, testing, troubleshooting, or documentation, including the ability to assess and refine generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering, including data sensitivity, secure handling of inputs and outputs, and relevant resilience and security practices; ability to advise colleagues on safe use.
• Working knowledge of Databricks Unity Catalog, Delta Live Tables, and declarative pipeline frameworks.
• Experience with AWS services such as S3, Glue, EMR, Lambda, and ECS/EKS.
• Experience implementing data quality, governance, and lineage solutions.
• Familiarity with data security controls, encryption, and PCI data-handling requirements.
• Preferred: Databricks Data Engineer Associate or Professional and/or AWS Solutions Architect or Developer certifications.
• Preferred: Experience with feature engineering platforms, such as Databricks Feature Engineering, Feature Store, Feature Views, or comparable enterprise technologies.
• Preferred: Experience developing scalable real-time and event-driven data solutions using technologies such as Kafka, streaming frameworks, and distributed service architectures.
• Preferred: Experience modernizing data platforms, including migrating legacy Spark or EMR workloads to Databricks-native architectures and adopting Lakehouse practices.
• Preferred: Knowledge of cloud-native data platform and governance technologies, including Terraform, Snowflake, Databricks SQL, Unity Catalog, and Infrastructure-as-Code practices.

• Design, develop, and support scalable Databricks feature engineering solutions for risk analytics, fraud detection, machine learning, and enterprise data products.
• Build and maintain reusable batch and real-time feature pipelines, covering onboarding, versioning, testing, monitoring, and lifecycle management within the Risk Feature Store ecosystem.
• Deliver modern data engineering solutions using Databricks, Apache Spark, PySpark, Delta Lake, Lakeflow, and declarative pipeline patterns, with an emphasis on scalability, resilience, and maintainability.
• Advance platform modernization by migrating legacy Spark and EMR workloads to Databricks-native architectures and applying cloud-native engineering practices.
• Use software engineering best practices, including CI/CD, automated testing, code reviews, observability, release management, and production support.
• Apply enterprise-approved AI-assisted engineering tools, such as GitHub Copilot and LLM Suite, to accelerate development, enhance code quality, automate SDLC activities, and identify opportunities for innovation.
• Use software development lifecycle tools, including authorized AI-assisted development and automation capabilities, to increase the value delivered through automation.
• Implement data quality, governance, lineage, and security controls in line with regulatory requirements, PCI standards, metadata management, retention policies, and audit expectations.
• Develop and support AWS cloud-native solutions using services such as S3, Glue, Lambda, ECS/EKS, and Aurora/RDS, along with Infrastructure-as-Code tools such as Terraform.
• Contribute to architecture reviews and technical decisions, recommending improvements to performance, operational stability, cost efficiency, resilience, and long-term scalability.
• Work with data scientists, model developers, business stakeholders, and engineering teams; mentor junior engineers and encourage reusable, secure, high-performing practices across the organization.

• AI
• AWS
• Lambda
• Architect
• CI/CD
• Cloud
• Copilot
• Databricks
• GitHub
• Support
• Kafka
• LLM
• Machine Learning
• Marketing
• Python
• PySpark
• SQL
• Security
• Snowflake
• Spark
• Terraform
• Unity
• GameDev

More:

We are partnering with hackajob to connect professionals with J.P. Morgan for a Senior Associate Data Engineer position in our Corporate Technology Risk organization. The role supports development and modernization of the Consumer and Community Banking Risk Feature Engineering Platform. As one of the worlds longstanding financial institutions, we serve consumers, small businesses, and corporate, institutional, and government clients through J.P. Morgan and Chase, with businesses spanning investment banking, consumer and commercial banking, transaction processing, and asset management. Our Corporate Functions teams work across areas including finance, risk, human resources, and marketing to support our businesses, clients, customers, and employees. We offer competitive total rewards and, depending on eligibility, benefits that may include healthcare, wellness resources, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. We are an equal opportunity employer and value diversity and inclusion; reasonable accommodations are available for religious practices and disability-related needs. Compensation and benefits details are provided during the hiring process.

last updated 41 week of 2026