Software Engineer III - Python​/PySpark​/Databricks​/AWS

Software Engineer III
- Python/PySpark/Databricks/AWS

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III
- Python/PySpark/Databricks/AWS at JPMorgan Chase within the Corporate Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

Job Responsibilities

• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
• Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
• Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
• Contributes to software engineering communities of practice and events that explore new and emerging technologies
• 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 applied experience
• Experience building and operating Databricks Lakehouse solutions hosted on Amazon Web Services (AWS), including Amazon S3, Identity and Access Management (IAM), Key Management Service (KMS), basic networking concepts (VPC/security groups), and logging/auditing.
• Experience using Delta Lake (ACID-compliant tables, partitioning strategies, schema evolution) and Apache Spark on Databricks, including performance optimization (cluster sizing, skew mitigation, join strategies, caching, and file sizing/compaction).
• Experience delivering batch and streaming data pipelines (Structured Streaming, incremental processing, backfills, late-arriving data handling) and implementing governance/security controls in Databricks (e.g., Unity Catalog, table/column-level permissions, credential passthrough where applicable), with operational ownership including monitoring/alerting, incident response, root-cause analysis (RCA), and service level objective/service level agreement (SLO/SLA) management.
• Advanced in one or more programming language(s) including Python, Py Spark
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• Architect Databricks Lakehouse solutions, including bronze/silver/gold (or equivalent) layering and domain-oriented data products; implement resilient, scalable ingestion from AWS sources into Databricks using batch and streaming patterns (including CDC where required).
• Build maintainable pipelines using Delta Live Tables (DLT) and/or Databricks Jobs/Workflows with modular design, documentation, and runbooks; ensure production readiness through retries, checkpointing, idempotency, safe re-runs, and defined replay/backfill procedures; implement testing practices including unit/integration tests, data quality checks, and contract testing;
  Apply governance-by-design controls (least privilege, PII classification, auditing, lineage/metadata, controlled sharing/consumption); optimize Spark/Delta performance and cost (cluster right-sizing, storage layout, job/warehouse spend); lead design/code reviews and mentor engineers; partner cross-functionally with stakeholders and security/platform teams; deliver CI/CD and infrastructure-as-code for Databricks + AWS with promotion across environments and strong version control/code review discipline.
• 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…