All fields marked * are required
• Elicit and document data requirements from QBE SMEs, product owners, and reporting/actuarial consumers; run structured workshops and follow-ups across time zones.
• Profile and analyse source system data (SQL against SQL Server/Oracle/Databricks) to assess quality, completeness, grain, and edge cases before build starts.
• Author precise source-to-target mapping (STM) specifications: field-level mappings, transformation and derivation logic, filter/join rules, SCD behaviour, reject/exception handling, and reference-data lookups.
• Define data quality rules and acceptance criteria per dataset; agree reconciliation and control totals with business and testing teams.
• Maintain the business glossary and metadata for owned subject areas; align terminology with the Data Modeler’s entities and the governance team’s definitions.
• Support engineering during build — clarify specs, adjudicate discrepancies, review outputs against mappings; support SIT/UAT with test-scenario design and defect triage.
• Perform impact analysis for change requests and source-system changes; keep STMs versioned and current.
• Produce clear analysis artefacts and walkthroughs for onshore stakeholders; convert ambiguous asks into decisions with documented assumptions.
• 9–12 years total with strong recent experience as a data/business analyst on data warehouse or lakehouse programmes.
• Advanced SQL for profiling and validation on large datasets (window functions, aggregation reconciliation); comfort querying Databricks (Spark SQL) directly.
• Proven authorship of source-to-target mappings and transformation specifications consumed by engineering teams.
• Insurance domain depth — P&C preferred: policy lifecycle, claims lifecycle, premium/exposure, bordereaux/reinsurance concepts, and typical KPI/reporting needs.
• Data quality analysis: rule definition, exception analysis, reconciliation approaches.
• Agile delivery experience with Jira/Confluence artefact discipline.
• Excellent stakeholder management and written documentation — specs a new engineer can build from without a meeting.
• Exposure to Databricks/PySpark concepts (enough to review engineering logic), Power BI semantic models, or actuarial data marts.
• Guidewire/Duck Creek or London-market data structures; ACORD standards.
• Python/pandas for ad-hoc analysis; Alteryx; data catalog tools (Purview/Collibra).
• Insurance certifications (III/CII/AINS) a plus.
• Bachelor’s degree; business, finance, statistics, or computer science preferred.
• Sharp requirements-to-decision facilitation; keeps ambiguity from reaching the sprint.
• Confident, concise English for daily onshore interaction and SME workshops.
• Detail obsession with an audit trail — every mapping decision traceable to a source and an owner.
Required
All fields marked * are required
• Elicit and document data requirements from QBE SMEs, product owners, and reporting/actuarial consumers; run structured workshops and follow-ups across time zones.
• Profile and analyse source system data (SQL against SQL Server/Oracle/Databricks) to assess quality, completeness, grain, and edge cases before build starts.
• Author precise source-to-target mapping (STM) specifications: field-level mappings, transformation and derivation logic, filter/join rules, SCD behaviour, reject/exception handling, and reference-data lookups.
• Define data quality rules and acceptance criteria per dataset; agree reconciliation and control totals with business and testing teams.
• Maintain the business glossary and metadata for owned subject areas; align terminology with the Data Modeler’s entities and the governance team’s definitions.
• Support engineering during build — clarify specs, adjudicate discrepancies, review outputs against mappings; support SIT/UAT with test-scenario design and defect triage.
• Perform impact analysis for change requests and source-system changes; keep STMs versioned and current.
• Produce clear analysis artefacts and walkthroughs for onshore stakeholders; convert ambiguous asks into decisions with documented assumptions.
• 9–12 years total with strong recent experience as a data/business analyst on data warehouse or lakehouse programmes.
• Advanced SQL for profiling and validation on large datasets (window functions, aggregation reconciliation); comfort querying Databricks (Spark SQL) directly.
• Proven authorship of source-to-target mappings and transformation specifications consumed by engineering teams.
• Insurance domain depth — P&C preferred: policy lifecycle, claims lifecycle, premium/exposure, bordereaux/reinsurance concepts, and typical KPI/reporting needs.
• Data quality analysis: rule definition, exception analysis, reconciliation approaches.
• Agile delivery experience with Jira/Confluence artefact discipline.
• Excellent stakeholder management and written documentation — specs a new engineer can build from without a meeting.
• Exposure to Databricks/PySpark concepts (enough to review engineering logic), Power BI semantic models, or actuarial data marts.
• Guidewire/Duck Creek or London-market data structures; ACORD standards.
• Python/pandas for ad-hoc analysis; Alteryx; data catalog tools (Purview/Collibra).
• Insurance certifications (III/CII/AINS) a plus.
• Bachelor’s degree; business, finance, statistics, or computer science preferred.
• Sharp requirements-to-decision facilitation; keeps ambiguity from reaching the sprint.
• Confident, concise English for daily onshore interaction and SME workshops.
• Detail obsession with an audit trail — every mapping decision traceable to a source and an owner.
Required