SAP BusinessObjects Data Services

Project Role : Custom Software Engineer
Project Role Description : Develop custom software solutions to design code and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable high-performing solutions tailored to specific business needs.
Must have skills : SAP BusinessObjects Data Services
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary
AI Powered Tech Talent. Build AI native data integration and data quality platforms using SAP BusinessObjects Data Services (BODS) by combining deep ETL data management and metadata expertise with agentic AI patterns (LLMs tools retrieval evaluation). This role focuses on moving beyond traditional batch ETL into intelligent self optimizing data pipelines that can reason about data structures detect anomalies recommend transformations and accelerate data modernizationwithout training foundation models from scratch.
Core Responsibilities
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:
oAnalyze source metadata and recommend transformation logic.
oPropose data quality rules based on observed patterns and historical issues.
oAuto-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 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.
6) Performance Scalability & Reliability
Optimize ETL jobs for performance and scalability (parallelism pushdown efficient 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.
7) 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.
8) 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.
Primary Skills (AI Native Must Have)
Strong hands on expertise in SAP BusinessObjects Data Services (BODS) ETL development and operations.
Solid understanding of data integration patterns transformation logic and enterprise data quality practices.
Experience designing reliable scalable data pipelines with performance and governance in mind.
AI native capability: tool augmented workflows retrieval grounded recommendations evaluation loops and safe automation boundaries.
Secondary / Strongly Beneficial Skills
Data warehousing and analytics fundamentals (facts dimensions hierarchies reconciliation).
Metadata management lineage concepts and data governance frameworks.
Experience integrating ETL platforms with cloud data ecosystems and modern analytics tools.
Scripting or automation skills to support pipeline orchestration and operational tooling.
What This Role Does Not Center On
Training or fine tuning foundation AI models.
Manual opaque ETL development without observability or measurable quality controls.
Value Delivered
Faster data pipeline development through intelligent ETL scaffolding and grounded recommendations.
Higher data trust via automated quality rules anomaly detection and evaluation loops.
Scalable modern data integration foundations that support analytics AI and enterprise decision making.
Additional Information
A 15 years full time education is required.