Remote Data Engineer – Databricks & Cloud Pipelines

Capgemini; Aguascalientes; Presencial; MXN 520.000 - 760.000; Jornada completa

Descripción de la vacante

Capgemini Engineering seeks a Data Engineer in a remote role focused on building scalable data pipelines with Databricks and Delta Lake. You will optimize ETL/ELT processes, ensure data quality, and enable analytics and ML workloads across cloud platforms such as Azure, AWS, or GCP.

You will collaborate with data scientists and analysts, implement data governance, and support DevOps practices for data workflows. A strong foundation in SQL and modern data tools is required.

Formación

• Hands-on data engineering or big data development experience.
• Strong knowledge of SQL variants (SQL, Oracle, SQL Server).
• Java/JavaScript and REST/SOAP APIs experience is a plus.
• Experience with Databricks Platform, Apache Spark, PySpark/Scala.
• Cloud platforms (Azure, AWS, or GCP) and data orchestration tools (Airflow, ADF).
• Data warehousing concepts (Star/Snowflake schemas) and Git/CI/CD.

Responsabilidades

• Design, develop, and support data replication and integration solutions using HVR.
• Build and maintain scalable data pipelines on Databricks (Spark, Delta Lake).
• Develop and optimize ETL/ELT processes for structured and unstructured data.
• Ensure data quality, governance, security, and access controls.
• Collaborate with data scientists and analysts for advanced analytics and ML workloads.
• Support CI/CD and DevOps practices for data engineering workflows.

Conocimientos

SQL

Python

Databricks

Spark

Power BI

REST/SOAP

JavaScript

ETL/ELT

Git

CI/CD

Educación

Bachelor’s degree in computer science, Engineering, or related field

Herramientas

Databricks Platform

Delta Lake

Azure

AWS

GCP

Airflow

Azure Data Factory

Synapse

Power BI

Descripción del empleo

Capgemini Engineering seeks a Data Engineer in a remote role focused on building scalable data pipelines with Databricks and Delta Lake. You will optimize ETL/ELT processes, ensure data quality, and enable analytics and ML workloads across cloud platforms such as Azure, AWS, or GCP.

You will collaborate with data scientists and analysts, implement data governance, and support DevOps practices for data workflows. A strong foundation in SQL and modern data tools is required.