Job Description
Summary
Empower business to transform analytics with SAP SAC, Datasphere, BDC, and Generative AI.
As a Junior SAP SAC Integration Engineer, you will support the design and development of SAP Analytics models and dashboards that provide meaningful business insights. Working under guidance from senior team members, you will help integrate SAC with core business systems, SAP Datasphere, and cloud platforms to contribute to a unified analytics ecosystem that enables faster, data‑driven decisions.
This role is well‑suited for Data Engineers with 2+ years of experience, with a strong interest in data analytics, system integration, GenAI, and digital transformation. In this role, you will work on building scalable integration components with BDC (SAP Business Data Cloud) and BTP Integration Suite (Business Technology Platform) to support agility and innovation.
Preferred candidates will have hands on experience with SAP ERP, a strong foundation in coding and scripting, and the ability to develop new skills as technology evolves.
If you’re looking to grow your skills while contributing to data projects that deliver measurable business value, this opportunity offers a strong foundation to begin shaping the future of enterprise technology.
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Responsibilities
- Develop data solutions that are flexible, extensible, elastic, secure and reliable at large scale.
- Work with Lead Data Engineer to provide guidance and direction to project teams ensuring compliance with coding standards and best practices.
- Collaborate with Data Governance team to capture and manage meta data, and implement data quality rules.
- Building and managing data pipelines, data products, integrations and promoting production.
- Develop Application Integrations, APIs and Microservices using hybrid cloud architecture.
- Continuously learn and be at the leading edge of Data/Application Integration, Cloud, Containerization, and other industry trends.
- Work with stakeholders including product, data and business teams to assist with data-related technical issues and support their data infrastructure needs.
- Follow Cyber security guidelines and polices to monitor the company's data security and privacy.
- Build and maintain batch data pipelines for structured and semi-structured data · Support ingestion and preprocessing of unstructured data.
- Implement basic data quality validations (schema checks, null checks).
- Assist in preparing AI-ready datasets for analytics, AI/ML and GenAI use-cases.
- Support implementation of data contracts through schema validation and data checks.
Knowledge, Skills and Abilities
- Practical experience in traditional and cloud data management components (MS SQL, RDS, Athena, or similar).
- Practical experience in metadata driven ingestion framework, building data pipelines and data sets.
- Working-level familiarity with DevOps and Agile methodologies.
- Strong analytical skills.
- Practical understanding of cloud security policies and concepts. · Exposure to data governance and quality tools.
- Experience with data integration, ETL/ELT orchestration, and application integration using APIs, messaging, or service-based architectures.
- Basic understanding of AI/ML data requirements (training vs inference datasets), structured, semi-structured and unstructured data processing.
- Exposure to streaming concepts, data parsing, text processing.
- Familiarity with data quality and observability concepts.
Education, Experience and Certifications
- Bachelor of Science Degree in MIS or Computer Science/Engineering (or similar) is required.
- Minimum of 2 years of Data Engineering experience.
- Experience developing microservices, serverless components, or distributed data processing solutions.
- Hands-on experience with at least one data integration, data pipelines or application integration platform.
- Practical scripting or programming experience across languages applicable to data engineering workloads.
- Cloud platform certification is preferred.
Physical Requirements
Willingness to travel if necessary