- 1.Implementation and Configuration: Lead or participate in the implementation of SAP Ariba solutions, including Ariba Procure-to-Pay (P2P), Ariba Sourcing, Ariba Contracts, Ariba Supplier Lifecycle and Performance (SLP), and Ariba Network. Configure and customize SAP Ariba applications to meet business requirements.
- 2. Business Process Analysis: Analyze procurement processes and requirements within the organization to identify areas for improvement and optimization using SAP Ariba solutions. Work closely with stakeholders to understand their needs and translate them into technical requirements.
- 3. Solution Design: Design end-to-end procurement solutions based on business requirements, leveraging SAP Ariba best practices and functionalities. Develop integration strategies between SAP Ariba and other SAP modules or third-party systems.
- 4. System Testing and Support: Conduct system testing including unit testing, integration testing, and user acceptance testing. Provide support during the testing phase and post-implementation support to resolve issues and optimize performance.
- 5. Training and Documentation: Develop training materials and conduct training sessions for end-users on SAP Ariba applications. Create documentation including user manuals, process flows, and configuration documents.
- 6. Collaboration: Collaborate with cross-functional teams including procurement, finance, IT, and business stakeholders to ensure alignment of procurement processes with overall business objectives. Work closely with SAP Ariba implementation partners and vendors as needed.
- 7. Continuous Improvement: Stay updated with the latest SAP Ariba developments and industry trends. Identify opportunities for continuous improvement in procurement processes and SAP Ariba system.
- Experience:
- • Demonstrated experience in SAP Ariba implementation and configuration.
- • Experience with one or more SAP Ariba modules such as Procure-to-Pay, Sourcing, Contracts, Supplier Lifecycle and Performance.
- • Knowledge of procurement processes and best practices.
- • Familiarity with integration between SAP Ariba and other SAP modules (such as SAP ERP) and external systems.
- 1.Technical Skills:
- • Proficiency in SAP Ariba configuration and customization.
- • Strong understanding of SAP Ariba APIs, integration methods, and data migration tools.
- • Knowledge of SAP Ariba Network and its functionalities.
- 2. Soft Skills:
- • Excellent communication and interpersonal skills.
- • Strong analytical and problem-solving abilities.
- • Ability to work independently as well as part of a team.
- • Customer-oriented approach with a focus on delivering high-quality solutions.
- 3. Certification:
- SAP Ariba certification is preferred but not mandatory.
- stakeholders and collaborating with cross-functional teams.
- Cloud cost management and best practices to optimize cloud resource usage and minimize costs.
Data Engineer – Preferred Qualifications:
- Experience working within the Azure ecosystem, including Azure AI Search, Azure Storage Blob, Azure Postgres and understanding how to leverage them for data processing, storage, and analytics tasks.
- Experience with techniques such as data normalization, feature engineering, and data augmentation.
- Ability to preprocess and clean large datasets efficiently using Azure Tools /Python and other data manipulation tools.
- Expertise in working with healthcare data standards (ex. HIPAA and FHIR), sensitive data and data masking techniques to mask personally identifiable information (PII) and protected health information (PHI) is essential.
- In-depth knowledge of search algorithms, indexing techniques, and retrieval models for effective information retrieval tasks. Familiarity with search platforms like Elasticsearch or Azure AI Search is a must.
- Familiarity with chunking techniques and working with vectors and vector databases like Pinecone.
- Experience working within the snowflake ecosystem.
- Ability to design, develop, and maintain scalable data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
- Experience with implementing best practices for data storage, retrieval, and access control to ensure data integrity, security, and compliance with regulatory requirements.
- Be able to implement efficient data processing workflows to support the training and evaluation of solutions using large language models, ensuring reliability, scalability, and performance.
- Ability to proactively identify and address issues related to data quality, pipeline failures, or resource contention, ensuring minimal disruption to systems.
- Experience with large language model frameworks, such as Langchain and know how to integrate them into data pipelines for natural language processing tasks.