We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer at JPMorgan Chase within Corporate Technology - Global Finance Technology, you serve as a seasoned member of an agile team that works to enhance, build, and deliver data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. As a core technical contributor, you are responsible for maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Deliver ingestion at scale: implement resilient ingestion from AWS sources into Databricks (batch + streaming), including CDC where needed.
- Build maintainable pipelines: use Delta Live Tables (DLT) and/or standard Jobs with clear modular structure, testing, and documentation.
- Operational excellence: productionize workloads via Databricks Workflows/Jobs, robust retries, checkpointing, idempotency, and safe re-runs.
- Governance by design: enforce least privilege, data classification (PII), auditing, lineage/metadata, and controlled sharing/consumption.
- Performance & cost management: tune Spark/Delta workloads, right-size clusters, optimize storage layout, and manage job/warehouse spend.
- CI/CD and IaC: Terraform (preferred) for Databricks + AWS resources; promotion across environments.
Testing: unit/integration tests for transformations, data quality checks, contract testing, and replay/backfill procedures. - Version control & code review discipline; clear documentation and runbooks
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years hands on Software Development Life Cycle experience
- Experience in data engineering experience building and maintaining data pipelines.
- Hands-on experience building and operating a Databricks Lakehouse Hosted in AWS
- Experience with Delta Lake (ACID tables, partitioning, schema evolution,
- Proven experience with Spark on Databricks (performance tuning, cluster sizing, skew mitigation, joins, caching, file sizing).
- Experience with streaming and batch pipelines (Structured Streaming; incremental processing; backfills; late-arriving data).
- Strong AWS fundamentals for data platforms: S3, IAM, KMS, networking basics (VPC/security groups), logging/auditing.
- Experience implementing data governance/security controls in Databricks (e.g., Unity Catalog, table/column permissions, credential passthrough patterns as applicable)
- Demonstrate experience with reliability: monitoring/alerting, incident response, RCA, and SLO/SLA management
- 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.
- 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.
Preferred qualifications, capabilities, and skills
- Familiarity with modern front-end technologies
- Exposure to cloud technologies