**What You'll Be Doing (Core Mission)**
- **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.
**Required Technical Skills & Experience**
**Engineering & Infrastructure**
- 5+ 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 **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.
**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.
**Minimum Qualifications**
- Bachelor's or Master's degree in **Computer Science**, **Engineering**, or related technical field.
- 4-6 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**.
**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/Aiops Engineer (México)
🏢 Oracle
📍 México