Design and implement large-scale fault-tolerant data pipelines on OCI using services like OCI Data Integration OCI Data Flow Apache Spark Object Storage and Autonomous Database Build and manage streaming data architectures using tools such as OCI GoldenGate Apache Kafka and Spark Flink Streaming Enforce standards and automation across the entire data lifecycle including schema evolution dataset migration and deprecation strategies Improve platform resilience data quality and observability with advanced monitoring alerting and automated data governance Serve as a technical leader mentoring junior engineers reviewing designs and code and promoting engineering best practices Collaborate cross-functionally with ML engineers platform teams and data scientists to integrate data services with AI ML workloads Partner in AI pipeline enablement ensuring Lakehouse services efficiently support model training feature engineering and real-time inference Minimum Qualifications Bachelor s or Master s degree in Computer Science Engineering or related technical field 3 year s experience designing and building cloud-based data pipelines and distributed systems Proficiency in at least one core language Python Java or Scala Familiar with lakehouse formats Iceberg Delta Hudi file formats Parquet ORC Avro and streaming platforms Kafka Kinesis Strong understanding of distributed systems fundamentals partitioning replication idempotency consensus protocols Engineering Infrastructure 3 years building distributed systems or production-grade data platforms in the cloud Strong coding proficiency in Python Java or Scala with an emphasis on performance and reliability Expertise in SQL and PLSQL data modeling and query optimization Proven experience with cloud-native architectures especially OCI AWS Azure or GCP Lakehouse Streaming Mastery Deep knowledge of modern lakehouse table formats Apache Iceberg Delta Lake or Apache Hudi Production experience with big data compute engines Spark Flink or Trino Skilled in real-time streaming and event-driven architectures using Kafka Flink GoldenGate or Streaming Experience managing data lakes catalogs and metadata governance in large-scale environments AI ML Integration Hands-on experience enabling ML pipelines from data ingestion to model training and deployment Familiarity with ML frameworks e g PyTorch XGBoost scikit-learn Understanding of contemporary ML architectures including RAG prompt chaining and agent-based workflows Awareness of MLOps practices including model versioning feature stores and integration with AI pipelines DevOps Operational Excellence Deep understanding of CI CD infrastructure-as-code IaC and release automation using tools like Terraform GitHub Actions or CloudFormation Experience with Docker Kubernetes and cloud-native container orchestration Strong focus on testing documentation and system observability Prometheus Grafana ELK stack Comfortable with cost performance tuning incident response and data security standards IAM encryption auditing Preferred Qualifications Experience with Oracle s cloud-native tools OCI Data Integration Data Flow Autonomous Database GoldenGate OCI Streaming Experience with query engines like Trino or Presto and tools like dbt or Apache Airflow Familiarity with data cataloging RBAC ABAC and enterprise data governance frameworks Exposure to vector databases and LLM tooling embeddings vector search prompt orchestration Solid understanding of data warehouse design principles star snowflake schemas and ETL optimization Soft Skills Team Expectations Proven ability to lead technical initiatives independently end-to-end Comfortable working in cross-functional teams and mentoring junior engineers Excellent problem-solving skills design thinking and attention to operational excellence Passion for learning emerging data and AI technologies and sharing knowledge across teams

📌 Senior Ai/big Data Engineer (India)
🏢 Oracle
📍 India