16 Sap Ds Jobs

You will be working independently as an SAP BODS developer, familiar with standard concepts, practices, and procedures. Your responsibilities will include experience in Data Extraction, Data Integration, Data Transformation, Data Quality, and Data Profiling. You should have experience in building integrations in SAP DS with Rest/SOAP APIs and cloud platforms like Google Cloud Platform. It is essential to possess a deep knowledge and understanding of Datawarehouse concepts and have strong PL-SQL skills for writing complex queries. You will be responsible for data extraction and integration using different data sources like SAP HANA and various warehouse applications. Experience in utilizing all kinds of SAP BODS transformations such as Data Integrator, Data Quality, and Basic Transforms is required. Additionally, tuning jobs for high volume and complex transformation scenarios and experience in DS components including job server, repositories, and service designers will be advantageous. In addition to the core responsibilities, you should be able to work with clients to identify business challenges and contribute to client deliverables by refining, analyzing, and structuring relevant data. It is crucial to stay aware of the latest technologies and trends in the industry. Logical thinking, problem-solving skills, and the ability to collaborate effectively are essential traits for this role. You must also be capable of assessing current processes, identifying improvement areas, and suggesting appropriate technology solutions. The mandatory skills for this position include expertise in SAP BODS.,

Key Responsibilities: Lead or support migrations from traditional L2/L3 data center networks to Cisco ACI using an application-centric approach. Perform application dependency mapping and define ACI constructs such as tenants, application profiles, EPGs, and contracts. Translate existing firewall rules, VLANs, and routing policies into ACI policy models. Deploy, configure, and troubleshoot ACI components including APIC, Nexus 9000 series switches, leaf/spine fabrics, and fabric access policies. Collaborate with application teams to ensure accurate mapping of workloads and security policies. Integrate ACI with virtualization environments (e.g., VMware vCenter, ESXi). Monitor and troubleshoot ACI fabric issues, using tools like APIC GUI/CLI, SNMP, and Syslog. Conduct detailed cutover planning, testing, and rollback strategies for ACI migrations. Integrate ACI with virtual environments (VMware, Hyper-V), firewalls, and load balancers. Document technical designs, migration plans, and operational procedures. Train operations staff and provide post-migration support to ensure environment stability. Required Qualifications: Bachelors degree in computer science, Network Engineering, or related field (or equivalent experience). 8+ years of experience in enterprise networking and 4+ years with Cisco ACI deployments. Strong understanding of data center networking, including VXLAN, routing protocols BGP, OSPF, multicast, and Layer 2/3 technologies. Experience with Cisco Nexus switches (especially Nexus 9000 series), APIC CLI/GUI, and troubleshooting ACI fabric issues. Strong understanding of ACI policy-driven networking, including bridge domains, contracts, L3Outs, and service graphs. Understanding of hybrid cloud networking and micro-segmentation concepts. Experience in ACI Migrations from Network-centric to application-centric migration in Cisco ACI. Demonstrated experience in application-centric ACI migrations, including dependency mapping and policy translation. Experience in analyzing existing network-centric environments, mapping applications to ACI constructs (tenants, EPGs, contracts), and executing migrations with minimal disruption. Strong troubleshooting and diagnostic skills. Preferred Skills: Hands-on experience with ACI Multi-Pod or Multi-Site environments. Knowledge of network automation (Python, Ansible) and ACI REST APIs. Experience with tools like Cisco NDP, MSO, or third-party app mapping solutions (e.g., Tetration, Algosec). Cisco certifications such as CCNP Data Center, Cisco ACI Field Engineer Specialist, or DevNet Professional

Overall 8+yrs of experience in which least 5 years of experience with SAP CI, DS and SAP API-M. A minimum of 4 years of project experience developing complex iflows. Deep knowledge of API frameworks and SAP CI connectors such as ODATA, SOAP, RESTful Services, IDoc, https, sftp, ABAP proxies.

Job Summary: We are looking for a highly skilled Senior Data Engineer with expertise in Snowflake, DBT (Data Build Tool), and SAP Data Services (SAP DS). The ideal candidate will be responsible for building scalable data pipelines, designing robust data models, and ensuring high data quality across enterprise platforms. Key Responsibilities: Design, build, and optimize data pipelines and ETL/ELT workflows using Snowflake and DBT Integrate and manage data from various sources using SAP Data Services Develop and maintain scalable data models, data marts, and data warehouses Work closely with data analysts, business stakeholders, and BI teams to support reporting and analytics needs Implement best practices in data governance, data lineage, and metadata management Monitor data quality, troubleshoot issues, and ensure data integrity Optimize Snowflake data warehouse performance (partitioning, caching, query tuning) Automate data workflows and deploy DBT models with CI/CD tools (e.g., Git, Jenkins) Document architecture, data flows, and technical specifications

Job Summary: We are looking for a highly skilled Senior Data Engineer with expertise in Snowflake, DBT (Data Build Tool), and SAP Data Services (SAP DS). The ideal candidate will be responsible for building scalable data pipelines, designing robust data models, and ensuring high data quality across enterprise platforms. Key Responsibilities: Design, build, and optimize data pipelines and ETL/ELT workflows using Snowflake and DBT Integrate and manage data from various sources using SAP Data Services Develop and maintain scalable data models, data marts, and data warehouses Work closely with data analysts, business stakeholders, and BI teams to support reporting and analytics needs Implement best practices in data governance, data lineage, and metadata management Monitor data quality, troubleshoot issues, and ensure data integrity Optimize Snowflake data warehouse performance (partitioning, caching, query tuning) Automate data workflows and deploy DBT models with CI/CD tools (e.g., Git, Jenkins) Document architecture, data flows, and technical specifications

Job Summary: We are looking for a highly skilled Senior Data Engineer with expertise in Snowflake, DBT (Data Build Tool), and SAP Data Services (SAP DS). The ideal candidate will be responsible for building scalable data pipelines, designing robust data models, and ensuring high data quality across enterprise platforms. Key Responsibilities: Design, build, and optimize data pipelines and ETL/ELT workflows using Snowflake and DBT Integrate and manage data from various sources using SAP Data Services Develop and maintain scalable data models, data marts, and data warehouses Work closely with data analysts, business stakeholders, and BI teams to support reporting and analytics needs Implement best practices in data governance, data lineage, and metadata management Monitor data quality, troubleshoot issues, and ensure data integrity Optimize Snowflake data warehouse performance (partitioning, caching, query tuning) Automate data workflows and deploy DBT models with CI/CD tools (e.g., Git, Jenkins) Document architecture, data flows, and technical specifications

Job Summary: We are looking for a highly skilled Senior Data Engineer with expertise in Snowflake, DBT (Data Build Tool), and SAP Data Services (SAP DS). The ideal candidate will be responsible for building scalable data pipelines, designing robust data models, and ensuring high data quality across enterprise platforms. Key Responsibilities: Design, build, and optimize data pipelines and ETL/ELT workflows using Snowflake and DBT Integrate and manage data from various sources using SAP Data Services Develop and maintain scalable data models, data marts, and data warehouses Work closely with data analysts, business stakeholders, and BI teams to support reporting and analytics needs Implement best practices in data governance, data lineage, and metadata management Monitor data quality, troubleshoot issues, and ensure data integrity Optimize Snowflake data warehouse performance (partitioning, caching, query tuning) Automate data workflows and deploy DBT models with CI/CD tools (e.g., Git, Jenkins) Document architecture, data flows, and technical specifications

Job Summary: We are looking for a highly skilled Senior Data Engineer with expertise in Snowflake, DBT (Data Build Tool), and SAP Data Services (SAP DS). The ideal candidate will be responsible for building scalable data pipelines, designing robust data models, and ensuring high data quality across enterprise platforms. Key Responsibilities: Design, build, and optimize data pipelines and ETL/ELT workflows using Snowflake and DBT Integrate and manage data from various sources using SAP Data Services Develop and maintain scalable data models, data marts, and data warehouses Work closely with data analysts, business stakeholders, and BI teams to support reporting and analytics needs Implement best practices in data governance, data lineage, and metadata management Monitor data quality, troubleshoot issues, and ensure data integrity Optimize Snowflake data warehouse performance (partitioning, caching, query tuning) Automate data workflows and deploy DBT models with CI/CD tools (e.g., Git, Jenkins) Document architecture, data flows, and technical specifications

Job Summary: We are looking for a highly skilled Senior Data Engineer with expertise in Snowflake, DBT (Data Build Tool), and SAP Data Services (SAP DS). The ideal candidate will be responsible for building scalable data pipelines, designing robust data models, and ensuring high data quality across enterprise platforms. Key Responsibilities: Design, build, and optimize data pipelines and ETL/ELT workflows using Snowflake and DBT Integrate and manage data from various sources using SAP Data Services Develop and maintain scalable data models, data marts, and data warehouses Work closely with data analysts, business stakeholders, and BI teams to support reporting and analytics needs Implement best practices in data governance, data lineage, and metadata management Monitor data quality, troubleshoot issues, and ensure data integrity Optimize Snowflake data warehouse performance (partitioning, caching, query tuning) Automate data workflows and deploy DBT models with CI/CD tools (e.g., Git, Jenkins) Document architecture, data flows, and technical specifications

Job Summary: We are looking for a highly skilled Senior Data Engineer with expertise in Snowflake, DBT (Data Build Tool), and SAP Data Services (SAP DS). The ideal candidate will be responsible for building scalable data pipelines, designing robust data models, and ensuring high data quality across enterprise platforms. Key Responsibilities: Design, build, and optimize data pipelines and ETL/ELT workflows using Snowflake and DBT Integrate and manage data from various sources using SAP Data Services Develop and maintain scalable data models, data marts, and data warehouses Work closely with data analysts, business stakeholders, and BI teams to support reporting and analytics needs Implement best practices in data governance, data lineage, and metadata management Monitor data quality, troubleshoot issues, and ensure data integrity Optimize Snowflake data warehouse performance (partitioning, caching, query tuning) Automate data workflows and deploy DBT models with CI/CD tools (e.g., Git, Jenkins) Document architecture, data flows, and technical specifications

Working independently as SAP BODS developer. Familiar with standard concepts, practices, and procedures. Experience in Data Extraction, data Integration, data transformation, data Quality, data profiling. Experience in building integration in SAP DS with Rest/SOAP APIs and cloud platforms like Google Cloud Platform. Possess a deep knowledge and understanding of Datawarehouse concepts. Have strong PL-SQL skills and experience in writing complex queries for validation and other purposes. Data extraction and integration using different data sources like SAP HANA, and different warehouse applications. Experience using all kind of SAP BODS transformations like Data Integrator, Data Quality and Basic Transforms. Tuning jobs for high volume and complex transformation scenarios. Experience in DS components including job server, repositories and service designers will be an added advantage. Additional Responsibilities: Ability to work with clients to identify business challenges and contribute to client deliverables by refining, analyzing, and structuring relevant data. Awareness of latest technologies and trends. Logical thinking and problem-solving skills along with an ability to collaborate. Ability to assess the current processes, identify improvement areas and suggest the technology solutions. Mandatory skills SAP BODS.

Key Responsibilities Architect and implement modular, test-driven ELT pipelines using dbt on Snowflake. Design layered data models (e.g., staging, intermediate, mart layers / medallion architecture) aligned with dbt best practices. Lead ingestion of structured and semi-structured data from APIs, flat files, cloud storage (Azure Data Lake, AWS S3), and databases into Snowflake. Optimize Snowflake for performance and cost: warehouse sizing, clustering, materializations, query profiling, and credit monitoring. Apply advanced dbt capabilities including macros, packages, custom tests, sources, exposures, and documentation using dbt docs. Orchestrate workflows using dbt Cloud, Airflow, or Azure Data Factory, integrated with CI/CD pipelines. Define and enforce data governance and compliance practices using Snowflake RBAC, secure data sharing, and encryption strategies. Collaborate with analysts, data scientists, architects, and business stakeholders to deliver validated, business-ready data assets. Mentor junior engineers, lead architectural/code reviews, and help establish reusable frameworks and standards. Engage with clients to gather requirements, present solutions, and manage end-to-end project delivery in a consulting setup Required Qualifications 5 to 8 years of experience in data engineering roles, with 3+ years of hands-on experience working with Snowflake and dbt in production environments. Technical Skills: o Cloud Data Warehouse & Transformation Stack: Expert-level knowledge of SQL and Snowflake, including performance optimization, storage layers, query profiling, clustering, and cost management. Experience in dbt development: modular model design, macros, tests, documentation, and version control using Git. o Orchestration and Integration: Proficiency in orchestrating workflows using dbt Cloud, Airflow, or Azure Data Factory. Comfortable working with data ingestion from cloud storage (e.g., Azure Data Lake, AWS S3) and APIs. Data Modelling and Architecture: Dimensional modelling (Star/Snowflake schemas), Slowly changing dimensions. ' Knowledge of modern data warehousing principles. Experience implementing Medallion Architecture (Bronze/Silver/Gold layers). Experience working with Parquet, JSON, CSV, or other data formats. o Programming Languages: Python: For data transformation, notebook development, automation. SQL: Strong grasp of SQL for querying and performance tuning. Jinja (nice to have): Exposure to Jinja for advanced dbt development. o Data Engineering & Analytical Skills: ETL/ELT pipeline design and optimization. Exposure to AI/ML data pipelines, feature stores, or MLflow for model tracking (good to have). Exposure to data quality and validation frameworks. o Security & Governance: Experience implementing data quality checks using dbt tests. Data encryption, secure key management and security best practices for Snowflake and dbt. Soft Skills & Leadership: Ability to thrive in client-facing roles with competing/changing priorities and fast-paced delivery cycles. Stakeholder Communication: Collaborate with business stakeholders to understand objectives and convert them into actionable data engineering designs. Project Ownership: End-to-end delivery including design, implementation, and monitoring. Mentorship: Guide junior engineers and establish best practices; Build new skill in the team. Agile Practices: Work in sprints, participate in scrum ceremonies, story estimation. Education: Bachelors or masters degree in computer science, Data Engineering, or a related field. Certifications such as Snowflake SnowPro Advanced, dbt Certified Developer are a plus.

We are looking for an experienced and results-driven Data Engineer to join our growing Data Engineering team. The ideal candidate will be proficient in building scalable, high-performance data transformation pipelines using Snowflake and dbt and be able to effectively work in a consulting setup. In this role, you will be instrumental in ingesting, transforming, and delivering high-quality data to enable data-driven decision-making across the clients organization. Key Responsibilities 1. Design and implement scalable ELT pipelines using dbt on Snowflake, following industry accepted best practices. 2. Build ingestion pipelines from various sources including relational databases, APIs, cloud storage and flat files into Snowflake. 3. Implement data modelling and transformation logic to support layered architecture (e.g., staging, intermediate, and mart layers or medallion architecture) to enable reliable and reusable data assets.. 4. Leverage orchestration tools (e.g., Airflow,dbt Cloud, or Azure Data Factory) to schedule and monitor data workflows. 5. Apply dbt best practices: modular SQL development, testing, documentation, and version control. 6. Perform performance optimizations in dbt/Snowflake through clustering, query profiling, materialization, partitioning, and efficient SQL design. 7. Apply CI/CD and Git-based workflows for version-controlled deployments. 8. Contribute to growing internal knowledge base of dbt macros, conventions, and testing frameworks. 9. Collaborate with multiple stakeholders such as data analysts, data scientists, and data architects to understand requirements and deliver clean, validated datasets. 10. Write well-documented, maintainable code using Git for version control and CI/CD processes. 11. Participate in Agile ceremonies including sprint planning, stand-ups, and retrospectives. 12. Support consulting engagements through clear documentation, demos, and delivery of client-ready solutions. Required Qualifications 3 to 5 years of experience in data engineering roles, with 2+ years of hands-on experience in Snowflake and DBT. Experience building and deploying DBT models in a production environment. Expert-level SQL and strong understanding of ELT principles. Strong understanding of ELT patterns and data modelling (Kimball/Dimensional preferred). Familiarity with data quality and validation techniques: dbt tests, dbt docs etc. Experience with Git, CI/CD, and deployment workflows in a team setting Familiarity with orchestrating workflows using tools like dbt Cloud, Airflow, or Azure Data Factory. Core Competencies: o Data Engineering and ELT Development: Building robust and modular data pipelines using dbt. Writing efficient SQL for data transformation and performance tuning in Snowflake. Managing environments, sources, and deployment pipelines in dbt. o Cloud Data Platform Expertise: Strong proficiency with Snowflake: warehouse sizing, query profiling, data loading, and performance optimization. Experience working with cloud storage (Azure Data Lake, AWS S3, or GCS) for ingestion and external stages. Technical Toolset: o Languages & Frameworks: Python: For data transformation, notebook development, automation. SQL: Strong grasp of SQL for querying and performance tuning. Best Practices and Standards: o Knowledge of modern data architecture concepts including layered architecture (e.g., staging, intermediate, marts, Medallion architecture). Familiarity with data quality, unit testing (dbt tests), and documentation (dbt docs). Security & Governance: o Access and Permissions: Understanding of access control within Snowflake (RBAC), role hierarchies, and secure data handling. Familiar with data privacy policies (GDPR basics), encryption at rest/in transit. Deployment & Monitoring: o DevOps and Automation: Version control using Git, experience with CI/CD practices in a data context. Monitoring and logging of pipeline executions, alerting on failures. Soft Skills: o Communication & Collaboration: Ability to present solutions and handle client demos/discussions. Work closely with onshore and offshore team of analysts, data scientists, and architects. Ability to document pipelines and transformations clearly. Basic Agile/Scrum familiarity working in sprints and logging tasks. Comfort with ambiguity, competing priorities and fast-changing client environment. Education: o Bachelors or masters degree in computer science, Data Engineering, or a related field. o Certifications such as Snowflake SnowPro, dbt Certified Developer Data Engineering are a plus.

We are looking for an experienced and results-driven Data Engineer to join our growing Data Engineering team. The ideal candidate will be proficient in building scalable, high-performance data transformation pipelines using Snowflake and dbt and be able to effectively work in a consulting setup. In this role, you will be instrumental in ingesting, transforming, and delivering high-quality data to enable data-driven decision-making across the clients organization. Key Responsibilities 1. Design and implement scalable ELT pipelines using dbt on Snowflake, following industry accepted best practices. 2. Build ingestion pipelines from various sources including relational databases, APIs, cloud storage and flat files into Snowflake. 3. Implement data modelling and transformation logic to support layered architecture (e.g., staging, intermediate, and mart layers or medallion architecture) to enable reliable and reusable data assets.. 4. Leverage orchestration tools (e.g., Airflow,dbt Cloud, or Azure Data Factory) to schedule and monitor data workflows. 5. Apply dbt best practices: modular SQL development, testing, documentation, and version control. 6. Perform performance optimizations in dbt/Snowflake through clustering, query profiling, materialization, partitioning, and efficient SQL design. 7. Apply CI/CD and Git-based workflows for version-controlled deployments. 8. Contribute to growing internal knowledge base of dbt macros, conventions, and testing frameworks. 9. Collaborate with multiple stakeholders such as data analysts, data scientists, and data architects to understand requirements and deliver clean, validated datasets. 10. Write well-documented, maintainable code using Git for version control and CI/CD processes. 11. Participate in Agile ceremonies including sprint planning, stand-ups, and retrospectives. 12. Support consulting engagements through clear documentation, demos, and delivery of client-ready solutions. Required Qualifications 3 to 5 years of experience in data engineering roles, with 2+ years of hands-on experience in Snowflake and DBT. Experience building and deploying DBT models in a production environment. Expert-level SQL and strong understanding of ELT principles. Strong understanding of ELT patterns and data modelling (Kimball/Dimensional preferred). Familiarity with data quality and validation techniques: dbt tests, dbt docs etc. Experience with Git, CI/CD, and deployment workflows in a team setting Familiarity with orchestrating workflows using tools like dbt Cloud, Airflow, or Azure Data Factory. Core Competencies: o Data Engineering and ELT Development: Building robust and modular data pipelines using dbt. Writing efficient SQL for data transformation and performance tuning in Snowflake. Managing environments, sources, and deployment pipelines in dbt. o Cloud Data Platform Expertise: Strong proficiency with Snowflake: warehouse sizing, query profiling, data loading, and performance optimization. Experience working with cloud storage (Azure Data Lake, AWS S3, or GCS) for ingestion and external stages. ' Technical Toolset: o Languages & Frameworks: Python: For data transformation, notebook development, automation. SQL: Strong grasp of SQL for querying and performance tuning. Best Practices and Standards: o Knowledge of modern data architecture concepts including layered architecture (e.g., staging ? intermediate ? marts, Medallion architecture). Familiarity with data quality, unit testing (dbt tests), and documentation (dbt docs). Security & Governance: o Access and Permissions: Understanding of access control within Snowflake (RBAC), role hierarchies, and secure data handling. Familiar with data privacy policies (GDPR basics), encryption at rest/in transit. Deployment & Monitoring: o DevOps and Automation: Version control using Git, experience with CI/CD practices in a data context. Monitoring and logging of pipeline executions, alerting on failures. Soft Skills: o Communication & Collaboration: Ability to present solutions and handle client demos/discussions. Work closely with onshore and offshore team of analysts, data scientists, and architects. Ability to document pipelines and transformations clearly. Basic Agile/Scrum familiarity working in sprints and logging tasks. Comfort with ambiguity, competing priorities and fast-changing client environment. Education: o Bachelors or masters degree in computer science, Data Engineering, or a related field. o Certifications such as Snowflake SnowPro, dbt Certified Developer Data Engineering are a plus.

Key Responsibilities Architect and implement modular, test-driven ELT pipelines using dbt on Snowflake. Design layered data models (e.g., staging, intermediate, mart layers / medallion architecture) aligned with dbt best practices. Lead ingestion of structured and semi-structured data from APIs, flat files, cloud storage (Azure Data Lake, AWS S3), and databases into Snowflake. Optimize Snowflake for performance and cost: warehouse sizing, clustering, materializations, query profiling, and credit monitoring. Apply advanced dbt capabilities including macros, packages, custom tests, sources, exposures, and documentation using dbt docs. Orchestrate workflows using dbt Cloud, Airflow, or Azure Data Factory, integrated with CI/CD pipelines. Define and enforce data governance and compliance practices using Snowflake RBAC, secure data sharing, and encryption strategies. Collaborate with analysts, data scientists, architects, and business stakeholders to deliver validated, business-ready data assets. Mentor junior engineers, lead architectural/code reviews, and help establish reusable frameworks and standards. Engage with clients to gather requirements, present solutions, and manage end-to-end project delivery in a consulting setup Required Qualifications 5 to 8 years of experience in data engineering roles, with 3+ years of hands-on experience working with Snowflake and dbt in production environments. Technical Skills: o Cloud Data Warehouse & Transformation Stack: Expert-level knowledge of SQL and Snowflake, including performance optimization, storage layers, query profiling, clustering, and cost management. Experience in dbt development: modular model design, macros, tests, documentation, and version control using Git. o Orchestration and Integration: Proficiency in orchestrating workflows using dbt Cloud, Airflow, or Azure Data Factory. Comfortable working with data ingestion from cloud storage (e.g., Azure Data Lake, AWS S3) and APIs. Data Modelling and Architecture: Dimensional modelling (Star/Snowflake schemas), Slowly changing dimensions. ' Knowledge of modern data warehousing principles. Experience implementing Medallion Architecture (Bronze/Silver/Gold layers). Experience working with Parquet, JSON, CSV, or other data formats. o Programming Languages: Python: For data transformation, notebook development, automation. SQL: Strong grasp of SQL for querying and performance tuning. Jinja (nice to have): Exposure to Jinja for advanced dbt development. o Data Engineering & Analytical Skills: ETL/ELT pipeline design and optimization. Exposure to AI/ML data pipelines, feature stores, or MLflow for model tracking (good to have). Exposure to data quality and validation frameworks. o Security & Governance: Experience implementing data quality checks using dbt tests. Data encryption, secure key management and security best practices for Snowflake and dbt. Soft Skills & Leadership: Ability to thrive in client-facing roles with competing/changing priorities and fast-paced delivery cycles. Stakeholder Communication: Collaborate with business stakeholders to understand objectives and convert them into actionable data engineering designs. Project Ownership: End-to-end delivery including design, implementation, and monitoring. Mentorship: Guide junior engineers and establish best practices; Build new skill in the team. Agile Practices: Work in sprints, participate in scrum ceremonies, story estimation. Education: Bachelors or masters degree in computer science, Data Engineering, or a related field. Certifications such as Snowflake SnowPro Advanced, dbt Certified Developer are a plus.

Overview The Enterprise Solutions SAP Integration Services Lead role is responsible for delivering, transitioning, and sustaining all application integrations that are being delivered across various projects that use common services and integration technologies including SAP PO, SAP DS, SAP SLT, SAP CI IS, SAP CI DS, and Ariba CIG. This is accomplished by building a skilled technical team to design, build, transition, and sustain each PepsiCo IT project while ensuring compliance with our governance standards and processes. Responsibilities Manages integration development, support, and governance team spread across different regions globally Engages functional and business teams to plan and implement integration solutions and ensures the right allocation of resources Monitors, evaluates and ensures projects are delivered on time, on budget, and with high quality Manages employees in Hyderabad IT hub and mentors team in their career within PepsiCo Provides guidance to team members and ensures patterns and best practices are followed through the lifecycle of the project. Actively engages other IT organizations and/or Business partners to address high priority integration issues and design fit-for-purpose solutions. Ensures compliance to corporate policies and maintains procedures and policies for integration applications in collaboration with other teams Removes roadblocks, cultivates relationships, and effectively communicates across Enterprise at various levels of leadership Qualifications Bachelor's degree in Computer Science or relevant disciplines with an IT emphasis is required. 10+ years of experience with integration applications with proven business and people results is required. Exposure to integration tools like SAP PO, SAP DS, SAP SLT, SAP CI IS, SAP CI DS, and Ariba CIG is a big plus Experience with integrations with large ERP applications like SAP Full lifecycle experience surrounding interfaces development - Requirements, Design, Data Mapping & Transformations, Development, Testing, Deployment and Production Support Excellent written and oral communication skills are a must Ability to multi-task and prioritize effectively in a fast-paced environment