Lead Software Engineer- Python / Java- (Cloud Data Platform — AWS/Databricks/Terraform)

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Develops secure and high-quality production code, and reviews and debugs code written by others
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Establishes clear “where AI helps / where it must not” guardrails for the SDLC (design, coding, testing, deployment, operations), ensuring sensitive data handling and secure engineering practices are consistently applied.
• Implements a repeatable AI-assisted review workflow (PR checklist, quality gates, and approval criteria) that improves readability, reduces defects, and enforces standards for maintainability, performance, and security.
• 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.
• Selects and standardizes the right tools across planning, code, build, test, security scanning, release, and operations—reducing fragmentation and improving end-to-end traceability.
• Designs CI/CD pipelines with measurable outcomes (lead time, deployment frequency, change failure rate), using automation to remove manual steps and improve release reliability.
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

• Formal training or certification on software engineering concepts and advanced applied experience Python
  Hands-on practical experience delivering system design, application development, testing, and operational stability
• Advanced in one or more programming language(s)
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Defines a practical operating model for AI-assisted development (approved use cases, do/don’t guidance, and escalation paths) so teams use tools consistently and responsibly.
• Establishes validation standards for AI outputs—mandatory peer review, secure coding checks, and automated test requirements—before any AI-assisted change is merged or released.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Proficient in all aspects of the Software Development Life Cycle
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• In-depth knowledge of the financial services industry and their IT systems
• Practical cloud native experience

Preferred qualifications, capabilities, and skills

• Advanced Apache Spark experience (PySpark/Spark SQL), including performance tuning (partitioning, shuffle optimization, caching), troubleshooting, and designing scalable batch/stream processing patterns.
• Deep lakehouse and data platform expertise (e.g., Delta Lake concepts, schema evolution, data quality frameworks, lineage/metadata, and governance patterns) applied in production environments.
• Strong DevSecOps and platform engineering capability, including CI/CD design, automated security/quality gates, observability (logs/metrics/traces), SLOs/SLAs, and incident management/RCA in regulated environments.
• Proven end-to-end delivery leadership for large, cross-functional initiatives (roadmaps, dependency management, stakeholder alignment, budgeting/capacity planning, and measurable outcomes tied to reliability, cost, and time-to-market).
• Experience driving responsible, enterprise-approved AI-assisted engineering adoption, including coaching teams on safe use, validation standards for AI outputs, and building reusable “golden path” templates/pattern libraries (Terraform modules, Databricks job templates, pipeline scaffolds).