Integration Architect

Integration Architect

Project Role : Integration Architect
Project Role Description : Architect an end-to-end integration solution. Drive client discussions to define the integration requirements and translate the business requirements to the technology solution. Activities include mapping business processes to support applications, defining the data entities, selecting integration technology components and patterns, and designing the integration architecture.
Must have skills : SAP BusinessObjects Data Services
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary
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 modernization—without 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:
o Analyze source metadata and recommend transformation logic.
o Propose data quality rules based on observed patterns and historical issues.
o 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 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.