About the Role
We are looking for an experienced Data Engineer with strong SAP Integration / SAP Data Extraction experience to join our team and build scalable enterprise data solutions.
The ideal candidate will have strong hands-on experience with Python, SQL, PySpark, Databricks, cloud platforms, and SAP data integration, with proven experience integrating SAP data into Databricks, Data Lakes, Data Warehouses, or other cloud-based data platforms.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines using Python, SQL, PySpark, and Databricks.
- Extract and integrate data from SAP systems into enterprise data platforms and cloud data lakes.
- Develop robust ETL/ELT pipelines for SAP data extraction, transformation, and processing.
- Work with SAP systems such as SAP S/4HANA, SAP ECC, SAP BW/BW4HANA, or similar platforms.
- Build data provisioning pipelines supporting Finance, reporting, analytics, and external reporting requirements.
- Optimize data pipelines, Spark jobs, SQL queries, and overall data-processing performance.
- Monitor, troubleshoot,
and resolve data-quality and pipeline operational issues.
- Develop scalable solutions for structured and unstructured data.
- Collaborate with SAP teams, Data Analysts, Data Scientists, business stakeholders, and engineering teams.
- Contribute to data architecture and future-state solutions for SAP-integrated data platforms.
- Implement and maintain CI/CD pipelines and DevOps practices for data engineering solutions.
- Ensure data pipelines meet standards for reliability, scalability, availability, and data quality.
Required Skills
- 8+ years of experience in Data Engineering / Software Engineering.
- Strong hands-on experience with: Python, SQL, PySpark / Apache Spark, Databricks
- 3+ years of experience working with big-data processing and cloud technologies.
- Robust experience writing and optimizing SQL queries against large and complex datasets.
- Hands-on experience with PySpark DataFr
📌 Data Engineer – SAP Integration (India)
🏢 Arccus
📍 India