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About the role
Company Description Aarohaan Sales and Services focuses on providing specialized services in niche business sectors, helping companies and small businesses grow in targeted areas. Guided by a partner for success philosophy, the organization aims to deliver end-to-end solutions that minimize effort for clients while maximizing outcomes. The company collaborates closely with small firms to support sustainable growth and improve operational efficiency. Core service offerings include recruitment and hiring, channel partnership, product sales and services, and personnel management. Role Description The bridge between QBE business/SME stakeholders and the engineering squad. You will own the analysis layer of the lakehouse build: profiling source data, defining source-to-target mappings, writing transformation rules and data-quality criteria, and validating that what engineering ships is what the business asked for across policy, claims, premium, and reinsurance subject areas. Key Responsibilities 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 Modelers entities and the governance teams 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. Must-Have Skills & Experience 912 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. Good-to-Have 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. Qualifications Bachelors degree; business, finance, statistics, or computer science preferred. Professional & Communication Skills 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. .
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