Business Data Architect

This role is accountable for producing the end-to-end data ow maps, data lineage
documentation and data landscape views that underpin Programme and the wider One
APAC Data Strategy. As a 2026 ll, the Business Data Architect will kick-start program pre-
work: translating the consolidated business data requirements (2,600+ prioritised data
elds across markets) into a validated, product-level view of what data exists at source,
what ows into each country Data Lake (staging, conformed and consumption zones) and
what is missing and must be hydrated.
The role sits at the intersection of business (Underwriting, Claims, Distribution,
Reinsurance, Actuarial, Finance), Data & Analytics, Data Engineering and Market IT. Where
the Data & Analytics Engineering (DAE) architects own the infrastructure and platform
architecture, this role owns the business view of data: how critical data items originate in
market core systems, how they move across systems and jurisdictions into the Data
Lakehouse, and how they map to the regional Product Hierarchy down to prot-centre
level. Success in this role depends on strong stakeholder management working hand-in-
hand with the Data Foundation Lead, Data & Analytics Programme Manager, Data
Engineers, Local Market IT and Regional and Market business stakeholders to produce data
ow maps that are accurate, validated and decision-ready.
Key Responsibilities:
1. Data Flow Mapping & Lineage
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Design and produce end-to-end data ow maps for critical data items from market
source/core systems, through Data Lake staging, zone 2 and consumption zone 3, to
downstream reporting, dashboards and analytics use cases.
Document data lineage and transformations for critical data elements identied in
the Master Data Requirements exercise, ensuring traceability that is audit-
defensible and supports regulatory, governance and AI-readiness needs.
Map data availability by country/Data Lake against the Product Hierarchy (to prot-
centre level), building the consolidated Commercial Data Landscape view of what
exists, where it sits (source only, staging, or consumption) and what is not captured
at all.
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Document cross-system dependencies and cross-border data ows, agging data
residency, privacy and regulatory considerations to the appropriate governance
forums.
2. Data Inventory, Data Model Alignment & Gap Analysis

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Own and mature the data asset inventory, catalogue and data dictionary aligned to
the consolidated Master Data Requirements Template resolving Not Specied,
TBD and validation required availability statuses through structured validation
with Market IT and Data Engineering.
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Align business data requirements to the conceptual and logical data model of the
target Data Lakehouse, ensuring the dependency between data model design and
lineage tracking of critical data items is explicitly documented.
Perform gap and hydration analysis by market and product, quantifying the scale of
data that must be brought into the Data Lakes and providing sizing input to the Data
Foundation Lead, DAE architects and Data Engineering delivery plans.
Contribute to the Data Consolidation Plan towards a single source of truth,
including recommended sequencing based on business priority (Top 200 elds,
Horizon 13 priorities).
3. Stakeholder Engagement & Workshops
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Work hand-in-hand with the Data Foundation Lead, Data & Analytics Programme
Manager, Data Engineers, DAE/infrastructure architects, Local Market IT and
Business stakeholders (Regional and Market) as the connective tissue between
business data needs and technical implementation.
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Plan and facilitate structured interviews and cross-functional validation workshops
with business, technology and data stakeholders to corroborate actual operating
data ows against documented processes establishing a fact-based, evidence-
backed view rather than relying on documentation alone.
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Translate ambiguous business asks into clear data denitions, mapping rules and
acceptance criteria that Data Engineering can build against, and play back ndings
to senior stakeholders in clear, visual, decision-oriented formats.
Manage competing priorities across multiple markets with tact, keeping momentum
in an environment of constrained capacity and diering local system landscapes.
4. Governance, Ownership & Documentation
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Produce and maintain the Data Ownership & Accountability Matrix mapping data
owners, stewards and custodians per data asset and highlighting ownership gaps
for remediation.

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Classify data assets by sensitivity and business function, aligning to Data Protection
Policy, PDPA and applicable regulatory expectations (e.g. MAS guidelines) in
partnership with Risk & Compliance.
Ensure all data ow maps, lineage records and inventory artefacts are version-
controlled, audit-ready and handed over cleanly into BAU data governance
processes.
Produce source to Datalake mapping document and data catalogue for missing
data elements to complete Datalake hydration
Key Skills
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Data ow mapping and lineage documentation proven ability to draw clear,
accurate source-to-consumption data ow maps across complex multi-system,
multi-market landscapes.
Data architecture and modelling strong grasp of conceptual/logical data models,
data lake/lakehouse zone architectures (staging, conformed, consumption), data
marts and MDM concepts.
Data inventory, catalogue, dictionary and metadata management; familiarity with
lineage/catalogue tooling (e.g. Purview, Collibra) and diagramming tools (e.g. Visio,
ErWin, Lucidchart).
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Hands-on data proling and validation skills (SQL essential) to verify what actually
exists in source systems and lake zones.
Strong stakeholder management this is critical: able to build trust and drive
outcomes across regional leadership, market business teams, Market IT and
engineering, and to facilitate eective cross-functional workshops.
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Insurance domain knowledge ideally commercial and specialty lines, with an
understanding of policy, claims, reinsurance and underwriting data.
Data quality, governance and privacy concepts, including cross-border data
considerations.
Clear written and visual communication able to make complex data landscapes
understandable to executives.

Experience & Qualication Requirements:
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8+ years experience in data architecture, data business analysis or information
architecture roles, with at least:
3+ years documenting data ows, lineage or data landscapes across multi-system /
multi-market environments.
Experience in Insurance or Financial Services, ideally within data foundation, data
lake or data governance programmes.
Demonstrated delivery of data inventory / data mapping / current-state data
landscape assessments involving both business and IT stakeholders.
Relevant certication (e.g. DAMA CDMP, TOGAF, cloud data platform certications)
preferred.