AI/ML Engineer

Ll Oefentherapie; México; Presencial; MXN 900,000 - 1,441,000; Jornada completa

Descripción de la vacante

A prominent data solutions company in Mexico is seeking an experienced data engineer to architect and optimize scalable big data solutions. The role involves developing ETL/ELT pipelines and managing data lakes and warehouses. Proficiency in Python and big data frameworks like Apache Spark is essential. The ideal candidate will ensure data quality and collaborate with colleagues for integration solutions. If you have strong skills in data processing and cloud technologies, this role offers exciting opportunities for growth.

Responsabilidades

• Architect, design, and optimize big data solutions for batch and real-time processing.
• Develop and maintain ETL/ELT pipelines for data ingestion and transformation.
• Integrate data from cloud applications, on-prem systems, APIs and streaming.
• Implement and manage data lakes and data warehouses solutions on cloud infrastructure.
• Ensure data consistency, quality, and compliance with governance standards.
• Collaborate with data architects and business stakeholders.

Conocimientos

Python

Java

Scala

Apache Spark

Hadoop

Hive

Flink

Kafka

ETL

SQL

NoSQL

Herramientas

Informatica

Talend

SSIS

Apache NiFi

dbt

MuleSoft

Dell Boomi

SnapLogic

Docker

Kubernetes

Descripción del empleo

Key responsibilities

• Architect, design, and optimize scalable big data solutions for batch and real-time processing.
• Develop and maintain ETL/ELT pipelines to ingest, transform, and synchronize data from diverse sources.
• Integrate data from cloud applications, on-prem systems, APIs, and streaming workspaces into centralized data repositories.
• Implement and managedata lakes anddata warehouses solutions on cloud infrastructure.
• Ensuredata consistency, quality, and compliance with governance and security standards.
• Collaborate with data architects, data engineers, and business stakeholders to align integration solutions with organizational needs.

Core qualifications

• Proficiency in Python, Java, or Scala for big data processing.
• Big Data Frameworks: Strong expertise in Apache Spark, Hadoop, Hive, Flink, or Kafka.
• Hands-on experience with data modeling, data lakes (Delta Lake, Iceberg, Hudi), and data warehouses (Snowflake, Redshift, BigQuery).
• ETL/ELT Development: Expertise with tools like Informatica, Talend, SSIS, Apache NiFi, dbt, or custom Python-based frameworks.
• APIs & Integration: Strong hands‑on experience with REST, SOAP, GraphQL APIs, and integration platforms (MuleSoft, Dell Boomi, SnapLogic).
• Data Pipelines: Proficiency in batch and real‑time integration (Kafka, AWS Kinesis/ Azure Event Hub/ GCP Pub/Sub).
• Databases: Deep knowledge of SQL (Oracle, PostgreSQL, SQL Server) and NoSQL (MongoDB, Cassandra, DynamoDB) systems.

Preferred experience

• Expertise with at least one major cloud platform (AWS, Azure, GCP).
• Experience with data services such as AWS EMR/Glue, GCP Dataflow/Dataproc, or Azure Data Factory.
• Familiarity with containerization (Docker) and orchestration (Kubernetes).
• Knowledge of CI/CD pipelines for data engineering.
• Experience with OCI and Oracle Database (including JSON/REST, sharding) and/or Oracle microservices tooling.

How we’ll assess

• Systems design interview: architect a scalable service; justify data models, caching, and failure handling.
• Coding exercise: implement and optimize a core algorithm/data‑structure problem; discuss trade‑offs.
• Code review: evaluate readability, testing, error handling, and security considerations.
• Practical discussion: walk through a past end‑to‑end project, metrics/SLOs, incidents, and learnings.