Enterprise Data Architect

Job Title: Enterprise Data Architect

Job Summary

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.

Education & Experience

• 8 - 10 years of Enterprise Data Modelling.
• Experience using major data modelling tools (examples: ERwin, ER/Studio, PowerDesigner, etc.).
• Experience with major database platforms (e.g. Oracle, SQL Server, Teradata, etc.).
• Understanding and experience with major Data Architecture philosophies (Dimensional, ODS, Data Vault, etc.).
• 5 - 8 years of management experience required.
• 5 - 8 years consulting experience preferred.
• Bachelor’s degree or equivalent experience, Master’s Degree Preferred.
• Experience in data analysis and profiling.
• Strong data warehousing and OLTP systems from a modelling and integration perspective.
• Strong understanding of data integration best practices and concepts.
• Strong development experience under Unix and/or Windows environments.
• Strong SQL skills required scripting (e.g., PL/SQL) preferred.
• Strong Knowledge of all phases of the system development life cycle.
• Understanding of modern data warehouse capabilities and technologies such as real-time, cloud, Big Data.
• Understanding of on premises and cloud infrastructure architectures (e.g. Azure, AWS, Google Cloud).