Enterprise Data Architect

Job Description

The Data Architect to lead Data Migration Lead / Enterprise Data Architect role with strong mainframe modernization, insurance domain knowledge, governance, and stakeholder management capabilities. 

Provides technical expertise in needs identification, data modelling, data movement and transformation mapping (source to target), automation and testing strategies, translating business needs into technical solutions with adherence to established data guidelines and approaches from a business unit or project perspective. 

Provides data understanding and coordinate data related activities with other data management groups such as master data management, data governance and metadata management. 

Leadership not only in the conventional sense, but also within a team we expect people to be leaders. Candidate should elicit leadership qualities such as Innovation, Critical thinking, optimism/positivity, Communication, Time Management, Collaboration, Problem-solving, Acting Independently, Knowledge sharing and Approachable

Essential Duties 

Data modelling & architecture

• Read, interpret and translate legacy data structures (VSAM, DB2 on z/OS, IMS/DL1, COBOL copybooks) into a modern target model (relational or event-driven)
• Design conceptual, logical and physical data models for the target platform
• Map relationships and dependencies between policy data, contracts, coverages and benefits (EB-specific)

Data inventory & lineage

• Build a complete data inventory - i.e. which data resides where on the mainframe and which programs use which files
• Document end-to-end data lineage (source transformation target)
• Determine ownership and classification (PII, financial, actuarial)

Data migration strategy

• Define the migration strategy - big bang vs. phased vs. dual-run per capability track
• Design ETL/ELT pipelines and substantiate the choice (e.g. ELT for maximum flexibility in the target system)
• Specify and validate migration rules and transformation logic
• Design rollback and fallback scenarios

Data quality & governance

• Define and measure data quality rules (completeness, accuracy, consistency, timeliness)
• Determine the data cleansing strategy before migration (fix at source vs. fix during migration)
• Set up a governance framework - ownership, stewardship, decision-making

Mapping & reconciliation

• Document source-to-target mapping at field level
• Define the reconciliation strategy - how do you prove the migration is correct?
• Align tolerances and acceptance criteria with the business 

Technical skills

• Understand mainframe data formats (EBCDIC, packed decimal, COMP-3, fixed-length records)
• Experience with modern data stacks (Kafka for event streaming, cloud data platforms)
• Master SQL and data analysis tools for impact and quality analyses

Stakeholder management & communication

• Navigate between technical teams   and business stakeholders
• Translate data migration risks into business impact for SteerCo reporting
• Maintain a clear RACI for all data activities per capability track

EB-specific context

• Define data scope and dependencies per track (e.g. policy administration vs. claims vs. customer onboarding)
• Align the sequence of data migration with the tiering  
• Coordinate with the calculation engine (Waterfall track) which actuarial data must be available when
• Coordinate dual-run validation - run mainframe and new platform in parallel and compare results

Graduate in Computer Science, Data Science, Information Technology, or a related field.