1) Enterprise Data Integration & ETL Engineering
Design, develop, and operate BODS data integration jobs for structured and semi structured data across SAP and non SAP systems.
Implement robust batch and near real time data pipelines supporting analytics, reporting, data warehousing, and downstream applications.
Build reusable data flows, workflows, and transforms aligned to enterprise data architecture standards.

2) Data Modeling, Transformation & Enrichment
Design complex transformation logic using BODS features such as queries, transforms, lookups, hierarchies, and reusable objects.
Implement data enrichment, standardization, and harmonization logic across multiple source systems.
Apply canonical data modeling practices to reduce duplication and point to point complexity.

3) Data Quality, Profiling & Governance
Implement data quality rules for validation, cleansing, matching, deduplication, and standardization.
Build profiling and validation pipelines to assess data completeness, accuracy, consistency, and timeliness.




Support governance requirements through lineage-aware jobs, audit trails, and traceable transformations.

4) AI Native Data Engineering (Agentic ETL Layer)
Build data engineering agents that can:
Analyze source metadata and recommend transformation logic.
Propose data quality rules based on observed patterns and historical issues.
Auto-generate initial ETL mappings and job scaffolding, validated against enterprise standards.
Implement retrieval grounded assistance that uses metadata catalogs, mapping documents, business rules, and historical defects to produce verifiable recommendations.
Enable conversational exploration of data pipelines (e.g., why did this record fail , what changed in yesterday s load ) with grounded, auditable outputs.

5) Testing, Validation & Evaluation Loops
Design automated validation strategies: schema checks, row counts, reconciliation rules, referential integrity checks, and regression co

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