Data Engineer (Handle EBCDIC data processing requirements)

Lorven Technologies Inc. is seeking a Data Engineer to handle EBCDIC data processing requirements. The role involves building and maintaining SQL Server stored procedures, developing Python utilities for data orchestration, and ensuring data integrity and compliance throughout the data lifecycle.

Responsibilities:

• Build and maintain SQL Server stored procedures for data archiving, purge eligibility determination, and purge execution (soft/hard deletes as required)
• Handle EBCDIC data processing requirements (e.g., EBCDIC↔ASCII/Unicode conversion, fixed-length records, packed decimals/COBOL-style encodings when applicable) and ensure data integrity post-conversion
• Develop Python utilities for orchestration, validation, reconciliation, exception handling, and operational automation (e.g., pre/post checks, file-based extracts, run reports)
• Implement purge controls: retention rules, legal hold/exclusions, referential integrity management, dependency ordering, and rollback/restore strategy
• Optimize performance for large volumes (partitioning strategies, indexing, batching, minimal logging approaches where appropriate, transaction scoping)
• Integrate and run jobs in Control M: job definitions, calendars, dependencies, resource limits, alerting, rerun/restart logic
• Create operational runbooks, monitoring dashboards/metrics, and support production incidents (root cause, fixes, postmortems)
• Ensure security and compliance: least-privilege access, audit trails, PII handling, and evidence for retention/purge execution
• Collaborate with DBAs, data governance, and application teams to validate retention policy interpretation and downstream impacts

Requirements:

• SQL Server: T SQL, stored procedures, transactions, error handling, temp tables, indexing, query tuning, SSMS, SQL Agent fundamentals (even if Control M is primary)
• EBCDIC: understanding of common code pages (e.g., 037/1047), conversion pitfalls, fixed-width layouts, and validation strategies
• Python: building maintainable scripts/modules for ETL-style tasks, database connectivity (ODBC), logging, configuration, and testability
• Batch scheduling: Control M job creation/execution, dependencies, alerts, reruns, and operational support
• Data lifecycle: archiving patterns, retention, purge frameworks, auditability, and safe-delete patterns
• Experience with very large tables (VLDB), table partitioning/switching, and archival to cheaper storage tiers
• Familiarity with mainframe-originated data models, COBOL copybooks, and packed numeric formats
• CI/CD for database objects (DACPAC/SSD T or equivalent), Python packaging, and automated testing
• Knowledge of data governance tools/processes and legal hold workflows
• Experience in regulated environments (SOX, PCI, HIPAA, GDPR-like retention controls)