Senior Data Engineer: Scale Pipelines & AI

Applaudo; Nuevo León; Presencial; MXN 1.167.804 - 1.527.128; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Private medical insurance

Modern Work Spaces

Entertainment area

Celebrations

Great work environment

Descripción de la vacante

A leading IT services company in Nuevo León is seeking a skilled Senior Data Engineer responsible for designing and maintaining scalable data pipelines and ingestion processes. You will collaborate closely with analytics and AI/Data Science teams, ensuring the quality and reliability of data. Ideal candidates will have over 5 years of experience, a strong background in SQL and Python, and the ability to work independently while adhering to best practices in data quality.

Formación

• 5+ years of experience as a Data Engineer, building and operating production-grade data pipelines.
• Proficiency in SQL, including advanced joins, CTEs, and performance tuning.
• Hands-on experience building ETL/ELT pipelines using modern orchestration tools.

Responsabilidades

• Design, build, and maintain scalable data pipelines.
• Implement and optimize ETL/ELT workflows with performance attention.
• Collaborate with analytics and product teams to understand data requirements.

Conocimientos

SQL

Python (PySpark)

Data modeling

ETL/ELT processes

Cloud platforms (AWS, Azure, GCP)

Git and CI/CD practices

Advanced English proficiency

Educación

Bachelor’s degree or higher in Computer Science or related field

Herramientas

Airflow

DBT

Databricks

PostgreSQL

Snowflake

Descripción del empleo

A leading IT services company in Nuevo León is seeking a skilled Senior Data Engineer responsible for designing and maintaining scalable data pipelines and ingestion processes. You will collaborate closely with analytics and AI/Data Science teams, ensuring the quality and reliability of data. Ideal candidates will have over 5 years of experience, a strong background in SQL and Python, and the ability to work independently while adhering to best practices in data quality.