Senior AI/Big Data Engineer

Overview

• 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.
  
  
  

• 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.
  
  
  

• 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.
  
  
  

• 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.
  
  
  

• 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 modern ML architectures: including RAG, prompt chaining, and agent-based workflows.
• Awareness of MLOps practices, including model versioning, feature stores, and integration with AI pipelines.
  
  
  

• 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).
  
  
  

• 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.
  
  
  

• 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.
  
  
  

Career Level - IC3

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