Open Position | DB2 Senior Engineer | Remote (Full-Time)

Role: DB2 Senior Engineer
Location: Oregon (Remote)

Job Description:-

Design and develop scalable data-engineering solutions supporting the migration of IBM DB2 data and workloads to the Databricks Lakehouse. Analyze DB2 database objects and dependencies, modernize SQL and legacy ETL workflows, validate migrated data, and optimize high-volume Databricks workloads. Collaborate with business, data engineering, cloud, and architecture teams on enterprise data-modernization initiatives.

Must Have Technical/Functional Skills

• IBM DB2 and AS400
• Databricks
• DB2-to-Databricks data migration
• ETL and ELT pipeline development
• DB2 schemas, tables, views, aliases, stored procedures, functions, triggers, and sequences
• DB2 Catalog SQL
• DB2 LUW command-line and SQL clients, including DBeaver and Data Studio
• Mainframe TSO/ISPF
• Lakebridge Analyzer
• Databricks Unity Catalog
• SQL and stored-procedure modernization
• Data-quality validation and reconciliation
• Python and PySpark
• Property and casualty insurance domain knowledge
• AI and generative AI capabilities for data engineering and migration, as an added advantage

Roles & Responsibilities

• Analyze and extract data from IBM DB2 databases for migration to the Databricks Lakehouse.
• Design and develop scalable ETL and ELT pipelines connecting DB2 and Databricks.
• Extract inventories of DB2 schemas, tables, views, aliases, stored procedures, functions, triggers, and sequences.
• Perform database-object and dependency validations.
• Convert DB2 SQL, stored procedures, and legacy ETL workflows into scalable Databricks solutions.
• Translate DB2 security grants and sensitive-data access controls into corresponding controls using Databricks Unity Catalog.
• Apply knowledge of DB2 structures and data models supporting property and casualty insurance products.
• Validate data quality, reconciliation, completeness, and migration accuracy between source and target platforms.
• Optimize high-volume data ingestion, transformations, Spark jobs, and Databricks workloads.
• Collaborate with business, data engineering, cloud, and architecture teams supporting data-modernization programs.
• Use Databricks AI and machine-learning capabilities to support AI-enabled data-engineering and migration solutions.
• Use generative AI or AI coding assistants to accelerate code analysis, conversion, documentation, and modernization.
• Apply large language models and prompt engineering to SQL transformation, code explanation, dependency analysis, and migration-artifact generation.
• Build or use AI-assisted data-quality and reconciliation frameworks to identify anomalies, schema mismatches, transformation discrepancies, and migration defects.
• Apply retrieval-augmented generation patterns to database metadata, data dictionaries, mapping documents, and migration knowledge bases.
• Develop Python- or PySpark-based automation utilities for metadata discovery, schema mapping, code conversion, test-case generation, and migration validation.
• Integrate AI-assisted development into CI/CD and data-engineering workflows.