Senior Project Manager Mainframe-to-Databricks Migration

Experience

• 12+ years of IT project/program management experience, with 5+ years leading large-scale legacy modernization or mainframe migration/decommissioning programs (USD 5M+ budgets, multi-year, multi-vendor).
• At least one completed mainframe-to-cloud or mainframe-to-distributed migration delivered end-to-end (through parallel run, cutover, and decommissioning), preferably to a Data Lake/big-data target (Databricks, Spark, Snowflake, EMR, or similar).
• 3+ years in insurance (life, P&C, or health) or an equivalently regulated financial-services environment, with direct exposure to regulatory/statutory reporting cycles and audit interactions.
• Demonstrated experience managing parallel-run and output-reconciliation programs where byte-level or tolerance-based parity was a formal acceptance criterion.
• Experience managing global, blended onshore/offshore delivery teams (15+ FTE) and third-party SI or code-conversion tooling vendors.

Technical Fluency (working knowledge; hands-on coding not required)

• Mainframe ecosystem understanding
• Target platform: Databricks architecture (workspaces, clusters/serverless compute, Delta Lake, Unity Catalog, Databricks Workflows), PySpark/Spark SQL processing paradigms, medallion/lakehouse design patterns, and cloud foundations (Azure or AWS)
• Data governance: data lineage, data quality frameworks, metadata/catalog management, retention and archival, and security models (RBAC, encryption, PII handling) in regulated environments.
• Scheduling/orchestration transition: CA-7, Control-M, or equivalent mainframe schedulers and their mapping to modern orchestration (Databricks Workflows, Airflow, ADF).

Project Management & Methodology

• Expert command of hybrid delivery: waterfall-style wave/cutover planning combined with Agile/Scrum execution for conversion and data engineering sprints.
• Strong financial management: estimation, forecasting, earned value or equivalent tracking, and vendor/SOW commercial management.
• Rigorous risk and quality management: RAID discipline, quality gates, entry/exit criteria, defect triage across functional, data, and parity defect classes.
• Tooling proficiency: MS Project/Smartsheet or equivalent, Jira/Azure DevOps, Confluence/SharePoint, and executive reporting (PowerPoint/dashboarding).

Domain & Regulatory Knowledge

• Insurance operations and data: policy administration, claims, billing, reinsurance, actuarial reserving concepts, and their batch processing cycles.
• Regulatory/reporting context: statutory reporting and state/DOI filing obligations, SOX/ITGC controls, internal and external audit evidence requirements, and data-privacy regulations (e.g., GLBA, state privacy laws; GDPR where applicable).
• Understanding of financial close, GL integration, and the criticality of month/quarter/year-end processing windows.

Leadership & Communication

• Executive presence: able to brief C-level steering committees, auditors, and regulators with clarity and confidence.
• Skilled negotiator and conflict resolver across business, IT, vendor, and compliance interests; able to hold the line on parity gates under schedule pressure.
• Proven ability to lead through ambiguity, manage retiring-workforce knowledge risk, and sustain team morale across a long-duration program.

• Prior hands-on background as a mainframe developer/analyst or data engineer earlier in career.
• Experience with automated code-conversion platforms and refactoring accelerators for COBOL-to-Spark/Java/Python transformation.
• Experience decommissioning mainframe estates: MIPS reduction tracking, software license retirement, and archive/retrieval solutions for retired data.
• Familiarity with reconciliation/parity tooling and test data management (masking/subsetting) in regulated environments.