Business Analyst
Skills: Digital : Python~Data Concepts & Data Modelling~PL/SQL
Experience Required: 6-8 Years
Role Descriptions:
Key Responsibilities
1. Data Analysis & Modeling Analyze complex data from multiple source systems to understand entities| relationships| business rules| and usage patterns (e.g.| policy| claims| finance| partner data).
Contribute to conceptual and logical data models for key domains| working in partnership with data architects and data modelers.
Recommend modeling patterns (e.g.| relational| dimensional) appropriate for warehouses| marts| and operational data stores| ensuring models are usable by downstream analytics and AI workloads.
Review and provide feedback on model changes| ensuring that naming| keys| cardinality| and business definitions are consistent and well documented.
2. Data Profiling & Data Quality Perform detailed data profiling on new and existing data sets (nulls| distributions| outliers| code sets| pattern and length analysis) using SQL and other tools to identify issues and opportunities.
Summarize profiling results in clear| consumable formats (tables| visuals| narratives) that explain data quality risks and their impact on business value and delivery scope.
Partner with data stewards| product owners| and engineers to translate profiling findings into data quality rules| checks| and monitoring requirements for pipelines and data products.
Support Trusted Data / AI-ready objectives by ensuring that quality expectations| thresholds| and controls are explicitly defined and testable for the assets you support.
3. Source Target Mapping & Design Lead or co-lead source target mapping for new data products and migrations| especially where legacy systems and cross-domain data need to be reconciled.
Validate mapping choices against business requirements| data types| formats| and constraints; identify and document transformation| derivation| and standardization logic.
Define and document completeness and reconciliation approaches (e.g.| row counts| hash totals| key coverage) that engineers and testers can automate in pipelines and tests.
Work closely with data engineers to ensure mappings are feasible| performant| and aligned to data golden-path patterns from raw harmonized curated zones.
4. Collaboration| Delivery & Run Participate actively in backlog refinement| design reviews| and sprint ceremonies to clarify data requirements| acceptance criteria| and non-functional needs (e.g.| latency| freshness| lineage| access controls).
Provide clear analytical input to estimates| tradeoffs| and risk assessment for stories and epics involving data sourcing| modeling| and mapping.
Support testing by defining test data needs| validating mapping implementations| and reviewing defects related to data issues (values| joins| business rules).
Contribute to incident/root-cause analysis for data issues and partner with engineering teams on durable fixes (not just one-off corrections).
5. Governance| Metadata & Documentation Create and maintain high-quality documentation for data definitions| mapping
Essential Skills: Data Analyst plays a hands-on role in understanding| modeling| and translating complex data from multiple source systems into trusted| analytics-ready data products that support business decision-making| regulatory reporting| and AI/analytics use cases.
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