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Custom Software Engineer
Bengaluru
Job No. atci-5219954-s1918220
Full-time
Job Description
Project Role : Custom Software Engineer
Project Role Description : Design, build and configure applications to meet business process and application requirements.
Must have skills : SAP BW/4HANA Data Modeling & Development
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary
Build AI native, data warehouse and analytics solutions on SAP BW/4HANA, combining deep BW/4HANA modeling expertise with agentic AI architectures (LLMs + tools + retrieval + evaluation). The focus is to evolve enterprise BI from reporting and dashboards to grounded, conversational analytics and data agents that can reason over governed models, retrieve context, and deliver explainable outcomes.
This skill is explicitly categorized as AI Native in
Core Responsibilities
1) Modern BW/4HANA Data Warehouse Engineering
Design and develop BW/4HANA models aligned to data warehousing and operational reporting needs.
Build on BW/4HANA principles as a modern packaged data warehousing solution that is highly optimized for SAP HANA and runs only on SAP HANA DB.
2) AI Native Analytics Experiences (Grounded + Governed)
Build LLM-powered experiences that answer business questions using retrieval + grounding over BW/4HANA models (e.g., queryable semantic artifacts), ensuring responses remain traceable to governed enterprise definitions. (Design intent / AI native pattern — not a claim about a specific SAP feature.)
Implement analytics agents that can interpret intent, retrieve the right context from modeled data, and provide outputs with controlled safety boundaries. (Design intent.)
3) Performance, Optimization & Multi Temperature Data Management
Engineer for enterprise-scale performance by applying optimization techniques and patterns relevant to BW on HANA and BW/4HANA.
Incorporate Data Tiering Optimization (DTO) concepts to manage hot/warm/cold data tiers where applicable.
4) Data Modeling, ETL/ELT & Integration Discipline
Deliver robust pipelines and transformations using BW on HANA / BW/4HANA data modeling and ETL concepts (including extraction/loading and reporting enablement as part of the warehouse lifecycle).
Ensure cross-system consistency and harmonization as part of the warehouse s role in the broader data architecture.
5) Evaluation, Reliability & Observability for AI Behaviors
Define evaluation loops (offline test sets + online monitoring) for AI-native analytics so changes to prompts/retrieval don t silently degrade accuracy. (Design intent.)
Add observability for AI interactions (grounding rate, failure modes, latency/cost) and build fallback behaviors for reliability. (Design intent.)
6) Rapid Prototyping Production Scale-Out
Prototype quickly (POC/pilot), demo frequently, and iterate—then harden solutions for production with enterprise controls. (Design intent.)
Publish reusable patterns/accelerators for modeling + AI-native consumption to help teams scale adoption. (Design intent.)
Primary Skills (AI Native Must Have)
SAP BW/4HANA data modeling & development and strong enterprise data warehousing fundamentals.
Understanding of BW/4HANA as a next-generation data warehouse optimized for HANA and its core design principles (e.g., modern interface, openness, high performance).
Hands-on capability to build LLM + RAG solutions (retrieval, grounding, prompt/tool design, evaluation) integrated with governed enterprise data. (AI-native build expectation.)
Engineering discipline: testability, reliability, performance mindset, and structured delivery. (General expectation.)
Secondary / Strongly Beneficial Skills
Experience with BW on HANA modeling tools and optimization practices (integration with HANA, data modeling/ETL, reporting/analytics, performance optimization).
Knowledge of DTO / multi-temperature management concepts in enterprise warehousing.
Familiarity with enterprise analytics enablement assets such as training/reference materials
Value Delivered
Faster time-to-insight through conversational, grounded analytics over governed BW/4HANA models.
Stronger trust and performance via BW/4HANA s HANA-optimized warehousing principles and DTO-aware design patterns.
Repeatable modernization and skills uplift through BW on HANA modeling/ETL and optimization practices.
Additional Information
A 15 years full time education is required
Project Role Description : Design, build and configure applications to meet business process and application requirements.
Must have skills : SAP BW/4HANA Data Modeling & Development
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary
Build AI native, data warehouse and analytics solutions on SAP BW/4HANA, combining deep BW/4HANA modeling expertise with agentic AI architectures (LLMs + tools + retrieval + evaluation). The focus is to evolve enterprise BI from reporting and dashboards to grounded, conversational analytics and data agents that can reason over governed models, retrieve context, and deliver explainable outcomes.
This skill is explicitly categorized as AI Native in
Core Responsibilities
1) Modern BW/4HANA Data Warehouse Engineering
Design and develop BW/4HANA models aligned to data warehousing and operational reporting needs.
Build on BW/4HANA principles as a modern packaged data warehousing solution that is highly optimized for SAP HANA and runs only on SAP HANA DB.
2) AI Native Analytics Experiences (Grounded + Governed)
Build LLM-powered experiences that answer business questions using retrieval + grounding over BW/4HANA models (e.g., queryable semantic artifacts), ensuring responses remain traceable to governed enterprise definitions. (Design intent / AI native pattern — not a claim about a specific SAP feature.)
Implement analytics agents that can interpret intent, retrieve the right context from modeled data, and provide outputs with controlled safety boundaries. (Design intent.)
3) Performance, Optimization & Multi Temperature Data Management
Engineer for enterprise-scale performance by applying optimization techniques and patterns relevant to BW on HANA and BW/4HANA.
Incorporate Data Tiering Optimization (DTO) concepts to manage hot/warm/cold data tiers where applicable.
4) Data Modeling, ETL/ELT & Integration Discipline
Deliver robust pipelines and transformations using BW on HANA / BW/4HANA data modeling and ETL concepts (including extraction/loading and reporting enablement as part of the warehouse lifecycle).
Ensure cross-system consistency and harmonization as part of the warehouse s role in the broader data architecture.
5) Evaluation, Reliability & Observability for AI Behaviors
Define evaluation loops (offline test sets + online monitoring) for AI-native analytics so changes to prompts/retrieval don t silently degrade accuracy. (Design intent.)
Add observability for AI interactions (grounding rate, failure modes, latency/cost) and build fallback behaviors for reliability. (Design intent.)
6) Rapid Prototyping Production Scale-Out
Prototype quickly (POC/pilot), demo frequently, and iterate—then harden solutions for production with enterprise controls. (Design intent.)
Publish reusable patterns/accelerators for modeling + AI-native consumption to help teams scale adoption. (Design intent.)
Primary Skills (AI Native Must Have)
SAP BW/4HANA data modeling & development and strong enterprise data warehousing fundamentals.
Understanding of BW/4HANA as a next-generation data warehouse optimized for HANA and its core design principles (e.g., modern interface, openness, high performance).
Hands-on capability to build LLM + RAG solutions (retrieval, grounding, prompt/tool design, evaluation) integrated with governed enterprise data. (AI-native build expectation.)
Engineering discipline: testability, reliability, performance mindset, and structured delivery. (General expectation.)
Secondary / Strongly Beneficial Skills
Experience with BW on HANA modeling tools and optimization practices (integration with HANA, data modeling/ETL, reporting/analytics, performance optimization).
Knowledge of DTO / multi-temperature management concepts in enterprise warehousing.
Familiarity with enterprise analytics enablement assets such as training/reference materials
Value Delivered
Faster time-to-insight through conversational, grounded analytics over governed BW/4HANA models.
Stronger trust and performance via BW/4HANA s HANA-optimized warehousing principles and DTO-aware design patterns.
Repeatable modernization and skills uplift through BW on HANA modeling/ETL and optimization practices.
Additional Information
A 15 years full time education is required
Qualifications
15 years full time education
