- 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.

- 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.

- 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.

- 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.

- Testing, Validation &
- Evaluation Loops

- Design automated validation strategies: schema checks, row counts, reconciliation rules, referential integrity checks, and regression comparisons.

- Establish evaluation harnesses for AI behaviors: golden datasets for transformations, accuracy checks for generated rules, and drift detection.

- Gate releases of ETL logic and AI-generated artifacts through measurable quality thresholds.

- Performance, Scalability &
- Reliability

- Optimize ETL jobs for performance and scalability (parallelism, pushdown, productive transforms, resource tuning).

- Implement error handling, restartability, idempotency, and recovery mechanisms to support reliable operations.

- Monitor pipelines and proactively identify bottlenecks, failures, or data degradation patterns.

- Operations, Monitoring &
- Incident Response

- Monitor job execution, data volumes, and quality metrics implement alerts aligned to SLAs and business impact.

- Perform root cause analysis for load failures and data issues document and automate preventive actions.

- Use AI augmented diagnostics to cluster recurring issues and recommend remediation steps grounded in runbooks and past incidents.

- Modernization &
- Platform Evolution

- Support modernization initiatives by integrating BODS pipelines with cloud data platforms and analytics ecosystems.

- Assist in transitioning legacy ETL logic toward more modular, metadata driven, and AI augmented data architectures.

- Collaborate with data architects, analytics teams, and platform engineers to deliver end to end data solutions.

📌 SAP BusinessObjects Data Services (Bengaluru)
🏢 Fittbot
📍 Bengaluru