Sr. Python Engineer - VP

About the role

This role is a VP Software Architect responsible for building, modernizing, and operating enterprise-scale data records archiving solutions that support Morgan Stanley’s regulatory, legal, and data lifecycle obligations.

Responsibilities

• Hands-On Engineering & Delivery
  • Build and modernize Python-based services, tools, and automation for data records archiving platforms.
  • Re-engineer legacy Perl/Shell/Java tooling used for: Archive ingestion and reconciliation, Drop-zone hygiene and monitoring, Entitlements and access controls, Disposition, retention validation, and reporting.
  • Personally lead complex refactors, performance tuning, and production issue resolution.
  • Implement CI/CD pipelines, test automation, and secure SDLC practices for archive-related systems.
  • Participate in incident response, RCA, and remediation for archive ingestion, retrieval, or compliance issues.
• Electronic Records Archiving Modernization & Migration
  • Modernize archival workflows across Archive360, ERA, and IBM CMOD platforms.
  • Drive migrations such as: ERA → Archive360 ingestion and metadata remediation, Legacy batch/script pipelines → Python-based orchestration, File-based/manual processes → resilient, observable services.
  • Ensure compliance with WORM, SEC 17a-4, retention schedules, legal/tax holds, and disposition controls.
  • Owning migration execution artifacts: cutover plans, rollback strategies, reconciliation evidence, and audit support.
• Architecture & Design
  • Define Python-first, performance-aware architectures for archiving platforms.
  • Make design decisions that balance: Cost efficiency, Processing speed and scalability, Regulatory risk and auditability.
  • Produce concise architecture artifacts (C4, ADRs, ingestion/disposition flows) that directly support efficient delivery.
  • Review designs and implementations to ensure efficiency considerations are embedded early—not added later.
• Cloud & Platform Enablement
  • Design and implement cloud-ready archive tooling (public/private/hybrid).
  • Implement infrastructure-as-code and environment parity for archive platforms.
  • Embed security controls: IAM, encryption, key management, entitlement enforcement, and audit logging.
• AI-Assisted Engineering & Innovation
  • Apply AI tools to accelerate: Code refactoring and modernization of legacy archive tooling, Automated test generation for ingestion and disposition workflows, Documentation and runbook creation.
  • Explore AI-assisted automation for archive operations (triage, anomaly detection, reconciliation support).
  • Ensure responsible AI usage with human oversight and compliance alignment.
• Collaboration, Governance & Influence
  • Partner closely with Records Management, Legal, Compliance, Data Governance, and Platform teams.
  • Mentor engineers on Python best practices, archive domain patterns, and regulatory-aware design.
  • Influence technical direction through execution quality and subject-matter expertise.
  • Communicate risks, tradeoffs, and progress clearly to senior technology leadership.

Requirements

• 12+ years of enterprise software engineering experience with strong modernization ownership.
• Advanced, hands-on expertise in Python for building production services and automation.
• Proven experience modernizing records archiving or data lifecycle platforms.
• Strong working knowledge of Perl, Shell, and Java for legacy assessment and migration.
• Deep understanding of Unix/Linux, batch processing, and high-volume file ingestion systems.
• Experience operating in regulated, audit-heavy environments.
• Technical Skills Core (Python-First):
  • Python (advanced): services, tooling, concurrency, packaging, dependency management.
  • Testing: unit/integration/contract testing for ingestion and disposition flows.
  • APIs, messaging, batch orchestration, idempotency and error handling.
  • Archiving Domain:
    • Archive ingestion, reconciliation, and retrieval patterns.
    • Retention schedules, legal/tax holds, WORM compliance.
    • Disposition workflows and evidence generation.
    • Platforms: Archive360, ERA, IBM CMOD (or equivalent).
  • Cloud & Operations:
    • Containers, orchestration platforms.
    • Infrastructure as Code, observability, performance tuning, RCA participation.
  • AI & Automation:
    • AI coding assistants and refactoring tools.
    • Workflow automation and agent-based tooling (nice to have).