As a Senior Associate Data Engineer within the Corporate Technology Risk organization, you will contribute to the development and modernization of the Consumer and Community Banking Risk Feature Engineering Platform. You will be expected to apply strong software engineering and data engineering practices while contributing to the platform's strategic direction through technical execution, innovation, automation, and continuous improvement.
Job Responsibilities
- Design, develop, and support scalable feature engineering solutions on Databricks that enable risk analytics, fraud detection, machine learning, and enterprise data products.
- Build and maintain reusable batch and real-time feature pipelines, including feature onboarding, versioning, testing, monitoring, and lifecycle management within the Risk Feature Store ecosystem.
- Implement modern data engineering solutions using Databricks, Apache Spark, PySpark, Delta Lake, Lakeflow, and declarative pipeline patterns, ensuring scalability, resiliency, and maintainability.
- Drive platform modernization initiatives by migrating legacy Spark and EMR workloads to Databricks-native architectures and adopting cloud-native engineering practices.
- Apply software engineering best practices including CI/CD, automated testing, code reviews, observability, release management, and production support to deliver high-quality, reliable solutions.
- Leverage enterprise-approved AI-assisted engineering tools such as GitHub Copilot and LLM Suite to accelerate development, improve code quality, automate SDLC activities, and identify opportunities for innovation.
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.
- Implement data quality, governance, lineage, and security controls, ensuring compliance with regulatory requirements, PCI standards, metadata management, retention policies, and audit expectations.
- Develop and support cloud-native solutions on AWS, utilizing services such as S3, Glue, Lambda, ECS/EKS, Aurora/RDS, and Infrastructure-as-Code technologies including Terraform.
- Participate in architecture reviews and technical decision-making, contributing recommendations that improve platform performance, operational stability, cost efficiency, resiliency, and long-term scalability.
- Collaborate with data scientists, model developers, business stakeholders, and engineering teams, while mentoring junior engineers and promoting reuse-first, secure, and high-performing engineering practices across the organization.
Required Qualifications, Capabilities and Skills
- Formal training or certification in software engineering, computer science, data engineering, or a related discipline with 3+ years of applied industry experience.
- Strong hands-on experience with Databricks, Apache Spark, PySpark, Delta Lake, and modern Lakehouse architectures.
- Experience building and supporting large-scale batch and streaming data pipelines.
- Proficiency in Python and SQL with a strong understanding of distributed computing principles.
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.
- Working knowledge of Databricks Unity Catalog, Delta Live Tables, and modern declarative pipeline frameworks.
- Experience with AWS cloud services including S3, Glue, EMR, Lambda, ECS/EKS, and related technologies.
- Experience implementing data-quality, data-governance, and lineage solutions.
- Familiarity with data security controls, encryption, and PCI data handling requirements.
Preferred Qualifications, Capabilities and Skills
- Professional certifications such as Databricks Data Engineer Associate/Professional and/or AWS Solutions Architect/Developer certifications are strongly preferred.
- Experience with Feature Engineering platforms including Databricks Feature Engineering, Feature Store, Feature Views, or similar enterprise feature management technologies.
- Hands-on experience building scalable real-time and event-driven data solutions, leveraging technologies such as Kafka, streaming frameworks, and distributed services architectures.
- Proven experience modernizing data platforms, including migration of legacy Spark/EMR workloads to Databricks-native architectures and adoption of Lakehouse best practices.
- Knowledge of cloud-native data platform technologies and governance, including Terraform, Snowflake, Databricks SQL, Unity Catalog, and Infrastructure-as-Code practices.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
Skills Required
- Formal training or certification in software engineering, computer science, data engineering, or a related discipline
- 3+ years of applied industry experience
- Hands-on experience with Databricks, Apache Spark, PySpark, Delta Lake, and modern Lakehouse architectures
- Experience building and supporting large-scale batch and streaming data pipelines
- Proficiency in Python and SQL
- Strong understanding of distributed computing principles
- Hands-on experience using enterprise-authorized AI-assisted software development tools, with ability to evaluate, validate, and refine generated outputs
- Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling, resiliency, and security expectations
- Working knowledge of Databricks Unity Catalog, Delta Live Tables, and declarative pipeline frameworks
- Experience with AWS services including S3, Glue, EMR, Lambda, ECS/EKS, and related technologies
- Experience implementing data-quality, data-governance, and lineage solutions
- Familiarity with data security controls, encryption, and PCI data handling requirements
- Databricks Data Engineer Associate or Professional certification
- AWS Solutions Architect or Developer certification
- Experience with feature engineering platforms, feature stores, feature views, or similar technologies
- Hands-on experience building scalable real-time and event-driven data solutions using Kafka, streaming frameworks, and distributed service architectures
- Experience modernizing data platforms by migrating legacy Spark or EMR workloads to Databricks-native architectures
- Knowledge of Terraform, Snowflake, Databricks SQL, Unity Catalog, and infrastructure-as-code practices
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
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Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
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Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
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Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
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JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
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