Technology->Business Intelligence - Reporting->SAP BW

Key Responsibilities:

• Lead end-to-end delivery of data engineering solutions leveraging Databricks and PySpark for scalable processing and transformation.
• Design, build, and optimize batch/near-real-time pipelines with strong focus on performance, cost efficiency, and reliability.
• Collaborate with SAP BW stakeholders to understand source models, data semantics, and reporting needs; translate them into up-to-date data platform designs.
• Develop reusable frameworks, coding standards, and best practices for Spark-based development and notebook/job orchestration.
• Ensure data quality through validations, reconciliation checks, and robust error handling/monitoring across pipelines.
• Drive technical discussions, estimate work, plan sprints, and manage delivery risks/dependencies for the team.
• Mentor engineers, conduct code reviews, and enforce secure, maintainable, and well-documented implementations.
• Partner with platform/DevOps teams to support CI/CD, environment management,



and production readiness for Databricks workloads. Minimum Qualifications:
• Bachelor’s or Master’s degree (BTECH, MTECH, MCA, MSC) in Computer Science, Engineering, or a related field.
• 7–9 years of overall experience with strong hands-on expertise in Databricks and PySpark for large-scale data processing.
• Strong experience working with SAP BW concepts and data structures to support analytics/reporting requirements.
• Solid understanding of data modeling, ETL/ELT patterns, and performance tuning in distributed data processing environments.
• Ability to lead technical delivery, collaborate with stakeholders, and communicate clearly across teams. Preferred Qualifications:
• Experience modernizing or integrating SAP BW data with cloud-based lakehouse architectures using Databricks.
• Strong knowledge of Spark optimization techniques (partitioning, caching, joins, file sizing) and production-grade pipeline design.
• Exposure to Delta L

📌 SAP BW (Hyderabad)
🏢 Infosys
📍 Hyderabad