Senior Data Engineer – AI Data Platform (OCI Lakehouse)

Oracle; Ciudad de México; Presencial; MXN 700,000 - 900,000; Jornada completa

Descripción de la vacante

A leading cloud solutions company in Mexico is seeking a Senior Data Engineer to design and implement large-scale data pipelines. The ideal candidate will have extensive experience with cloud-native architectures and streaming data solutions, along with strong proficiency in programming languages like Python and SQL. This role offers competitive benefits and the opportunity to work in a collaborative environment focused on innovation.

Servicios

Competitive salary

Flexible medical insurance

Retirement options

Community volunteer programs

Formación

• 5+ years of experience building cloud-based data pipelines.
• Strong proficiency in distributed systems and performance optimization.
• Experience with big data and event-driven architectures.

Responsabilidades

• Design and implement data pipelines using OCI services.
• Build and manage streaming architectures with Kafka and Flink.
• Collaborate with cross-functional teams for AI/ML services.

Conocimientos

Distributed systems

Python

SQL and PLSQL

CI/CD

Kafka

Docker

Data modeling

Cloud-native architectures

Data security standards

Machine Learning frameworks

Educación

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

Herramientas

Oracle Cloud Infrastructure (OCI)

Apache Kafka

Terraform

Kubernetes

Apache Airflow

Descripción del empleo

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.

Career Level - IC3

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