Data Engineer
Data Engineer in Oracle's customer-facing CSS Innovation Studios in Bengaluru, designing and modernizing AI-ready data platforms for enterprise customers on Oracle AI Data Platform and OCI. Day to day: building PySpark/Delta Lake pipelines, tuning Spark workloads, supporting migrations, and technical consulting.
- Compensation
- Not disclosed
- Location
- BENGALURU, KARNATAKA, India
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5–8 years of experience designing, building, and delivering scalable data platforms
- Proven experience as a Data Engineer, Software Engineer, AI Engineer, Consulting Engineer, or similar technical role
- Strong knowledge of modern cloud data platforms like Databricks
- Strong hands-on experience with Apache Spark, PySpark, Delta Lake, Python, SQL, and modern data ecosystems
- Exposure to building AI agents and orchestration pipelines
- Strong understanding of Spark architecture, configuration, distributed systems, performance optimization, and tuning
- Experience modernizing analytics platforms and refactoring Spark and ETL workloads
- Working understanding of networking concepts to support data connectivity, configuration, and troubleshooting
- Working knowledge of DataOps, CI/CD, Infrastructure as Code, data security, governance, metadata, lineage, and observability
- Strong communication, customer collaboration, and stakeholder-management skills
Nice to have
- Experience with Scala, Java, or Kafka is a plus
Full description
CSS Innovation Studios is seeking Data Engineers with strong PySpark and data engineering expertise to deliver AI and data-transformation programs for Oracle’s enterprise customers.
In this customer-facing role, you will design, build, modernize, and operationalize scalable data platforms and AI-driven data solutions on Oracle AI Data Platform (AIDP) and Oracle Cloud Infrastructure (OCI). You will collaborate with customer technical teams, solution architects, engineering leaders, and Oracle product teams to accelerate AI adoption, modernize data estates, and deliver secure, high-performing production solutions.
The role combines hands-on software and data engineering with technical consulting. You will develop scalable systems for generative AI, large language models, real-time analytics, and modern data workloads; support platform implementations and migrations; and partner with architects, product managers, and engineers to apply Oracle product capabilities in customer deployments.
- 5–8 years of experience designing, building, and delivering scalable data platforms.
- Proven experience as a Data Engineer, Software Engineer, AI Engineer, Consulting Engineer, or similar technical role.
- Strong knowledge of modern cloud data platforms like Databricks.
- Strong hands-on experience with Apache Spark, PySpark, Delta Lake, Python, SQL, and modern data ecosystems; experience with Scala, Java, or Kafka is a plus.
- Exposure to building AI agents and orchestration pipelines.
- Strong understanding of Spark architecture, configuration, distributed systems, performance optimization, and tuning.
- Experience modernizing analytics platforms and refactoring Spark and ETL workloads.
- Working understanding of networking concepts to support data connectivity, configuration, and troubleshooting.
- Working knowledge of DataOps, CI/CD, Infrastructure as Code, data security, governance, metadata, lineage, and observability.
- Strong communication, customer collaboration, and stakeholder-management skills.
Design and deliver Oracle AI Data Platform solutions, including platform implementations and migrations.
- Build scalable, governed, AI-ready data pipelines, software components, and services for data consolidation, integration, transformation, and data products.
- Develop Lakehouse, Medallion, streaming, and real-time analytics solutions on OCI using Spark, PySpark, Delta Lake, Parquet, Flink, and distributed-systems patterns.
- Build AI/ML and Generative AI data pipelines, including feature engineering, feature extraction, MLOps workflows, and vector-ready datasets.