Data Analyst -Insurance Domain

Job Description – Data Engineer (Azure Synapse & Data Platform) Role Overview We are looking for a technically strong Data Engineer to join an existing enterprise data platform team. This is not a traditional reporting analyst role — we need someone who is equally comfortable writing complex SQL and understanding the underlying data architecture as they are surfacing insights. The primary focus will be on interrogating foundational data products, tracing data lineage across platform layers, and identifying gaps, inconsistencies, and quality issues in what has been built. This person will act as a critical bridge between the engineering team and the business, helping to validate what the platform produces and surface what is missing or incorrect.

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Key Responsibilities • Interrogate foundational and use case data products across the platform to assess completeness, correctness, and consistency • Write complex SQL queries to explore, profile, and validate data across multiple layers — from raw ingestion through to conformed and consumption datasets • Trace data lineage end-to-end: understand where data originates, how it is transformed at each layer, and what reaches the consumption layer • Identify data gaps, anomalies, unexpected transformations, and discrepancies between source systems and downstream data products • Document findings clearly — translating technical observations into structured gap analyses and data quality assessments that both engineers and business stakeholders can act on • Work closely with senior data engineers and the solution architect to provide ground-level data evidence that informs re-engineering and migration decisions • Validate business logic embedded in transformation layers by comparing expected versus actual outputs across datasets • Support data governance efforts by profiling datasets, cataloguing findings, and contributing to data quality rule definitions • Collaborate with business and analytics stakeholders to understand expected data behaviour and reconcile against what the platform currently delivers • Contribute to the development of semantic views, analytical datasets, and self-serve reporting assets where required

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Must-Have Skills SQL & Data Querying • Expert-level SQL — able to write complex analytical queries involving multi-table joins, window functions, aggregations, CTEs, and recursive logic • Strong ability to profile and explore unfamiliar datasets without prior documentation • Experience querying large-scale data platforms — comfortable working with partitioned tables, Delta tables, and distributed query engines • Experience with Azure Synapse SQL Pool and/or Synapse Serverless SQL • Ability to reverse-engineer data transformations by reading query outputs and comparing across layers

Data Platform & Architecture Literacy • Solid understanding of medallion / lakehouse architecture (Raw / Harmonized / Conformed / Consumption) — able to navigate a multi-layer platform and understand the purpose and content of each layer • Familiarity with data modelling concepts: surrogate keys, slowly changing dimensions, denormalization, conformed dimensions • Understanding of CDC and SCD patterns and their impact on historical data • Ability to read and interpret data pipeline logic — not necessarily build it, but understand what a pipeline is doing and whether the output is correct • Experience working with Azure Data Lake Storage Gen2 and Delta Lake format (Parquet, Delta tables) • Familiarity with Azure Synapse Analytics environment — navigating SQL Pools, Spark outputs, and storage layers • Must have strong insurance-domain knowledge, including familiarity with core insurance concepts and data such as policies, products, premiums, claims, customers, sales, and CRM structures

Analytics Engineering • Experience building or contributing to semantic layers, data models, or analytical datasets consumed by BI tools • Familiarity with dbt or similar analytics engineering frameworks is a plus • Ability to translate business questions into well-structured analytical data models • Experience with Power BI or equivalent BI tools — understanding of how semantic models consume underlying data products Data Quality & Investigation • Strong analytical mindset — able to form hypotheses about data issues, design SQL-based tests to validate them, and clearly document conclusions • Experience with data profiling: null rates, cardinality, referential integrity checks, distribution analysis • Ability to compare datasets across systems or layers and surface meaningful discrepancies • Familiarity with data quality frameworks and rule-based validation approaches

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Nice to Have • Exposure to Python (Pandas / PySpark) for data exploration beyond SQL • Familiarity with Azure Purview or Unity Catalog for data cataloguing and lineage • Experience with dbt for analytics engineering and data model documentation • Exposure to data observability or monitoring tooling

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Experience & Profile • 5–8+ years of experience in a data engineering role, with strong SQL, data platform, and analytical capabilities • Demonstrated ability to work directly with complex, undocumented, or legacy datasets and make sense of them independently • Comfortable operating in ambiguity — this role requires curiosity and persistence when the data does not behave as expected • Strong documentation skills — able to produce clear gap analyses, data dictionaries, and investigation findings that non-technical stakeholders can understand • Collaborative and inquisitive — works well embedded within an engineering team while also engaging directly with business users • Detail-oriented without losing sight of the bigger picture — able to flag granular data issues in the context of platform-level quality and completeness • Experience working in regulated or enterprise-scale environments (financial services a plus)