Data Analyst

Mandatory Skills :

Expert SQL, Azure Synapse SQL Pools, Medallion Architecture, Data Lineage, Data Profiling, Data Quality & Gap Analysis, Data Lake Storage (ADLS Gen2/Delta Lake), Data Modeling, Power BI

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 Interna

Core Technical Skills

SQL & Data Querying

• Expert-level SQL able to write complex analytical queries involving multi-tablejoins, 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

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 Interna

• 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

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
• Background in financial services or insurance data (policy, sales, CRM data structures)

Experience & Profile

• 5–8+ years of experience in a data analyst, analytics engineering, or BI engineering role with a strong technical focus
• 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)