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 up-to-date 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.

Preferred Qualifications

- 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.

Program &

- Delivery Management

- Own the end-to-end migration roadmap: application/job inventory, complexity assessment, wave planning, and sequencing of COBOL/JCL batch streams, DB2 and VSAM data stores, and GDG-based file chains into Databricks/PySpark workloads.

- Build and maintain the integrated program plan (scope, schedule, budget, resourcing, dependencies, critical path) across discovery, code conversion/refactoring, data migration, testing, parallel run, cutover, and legacy decommissioning phases.

- Manage blended onshore/offshore teams of mainframe SMEs, data engineers, Databricks/PySpark developers, QA/parity testers, and business analysts; coordinate with system integrators and automated code-conversion tool vendors where applicable.

- Run steering committees, executive status reporting, RAID (risks, assumptions, issues,



dependencies) management, and change control; escalate with data-driven options and recommendations.

- Control program financials: budget forecasting, burn tracking, vendor SOW/contract management, and business-case benefit tracking (MIPS/MSU reduction, license retirement, run-cost savings).

- Manage known-difference registers: document, justify, and obtain business/compliance sign-off for every intentional deviation (rounding behaviour, EBCDIC-to-ASCII/Unicode conversion effects, packed-decimal/COMP-3 precision, sort-order and collation differences, date/timestamp handling).

- Embed data quality, lineage, and reconciliation controls into the target platform (e.g., Unity Catalog lineage, automated reconciliation jobs) so parity is continuously demonstrable, not a one-time event.

Downstream Compatibility &

- Cutover

- Inventory and protect all downstream consumers: fixed-width/copybook-formatted extracts, FTP/MFT file transfers, vendor feeds, GL and actuarial interfaces, document generation, and reporting marts - preserving file layouts, naming conventions, delivery schedules, and SLAs, or managing coordinated consumer changes.

- Govern replacement of mainframe constructs with functional equivalents: JCL job streams and scheduler dependencies (CA-7/Control-M/Zeke) to Databricks Workflows/orchestration
- GDG generation semantics to versioned/partitioned datasets with equivalent retention and rollback behavior.

- Plan and execute cutover and rollback strategies per wave, including data freeze/catch-up approaches, dual-write or replay mechanisms, hypercare support models, and decommissioning gates.

- Coordinate historical data migration and archival strategy for DB2/VSAM/GDG data, including retention compliance and audit retrieval requirements.

Stakeholder &

- Compliance Management

- Act as the primary interface between technology teams and business stakeholders in actuarial, underwriting, claims, finance, and compliance; translate parity/technical issues into business impact and decisions.

- Drive organizational readiness: operational runbooks, support model transition (mainframe ops to platform/data engineering ops), training, and knowledge transfer from retiring mainframe SMEs.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Project Manager - Mainframe-to-Databricks Migration (Bengaluru)
🏢 Visionet Systems
📍 Bengaluru