Data Engineer II — Cloud Data Pipelines & Analytics

JLL; Jalisco; On-site; MXN 600,000 - 900,000; Full time

Job summary

JLL Technologies seeks a Data Engineer P2 to join the Enterprise Data team in a global, self-starting role. You will design and develop data solutions built on current technologies and patterns, collaborating with JLLT teams worldwide.

The role focuses on cloud data engineering, ETL, and data quality, partnering with data scientists and analysts to deliver scalable data infrastructure and pipelines.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, or related field.
• 3–5 years of experience in data engineering or full-stack development with a cloud focus.

Responsibilities

• Design, develop, and maintain scalable cloud-based data infrastructure using SQL and PySpark.
• Collaborate with cross-functional teams to understand data requirements and define ingestion architecture.
• Design and implement data pipelines feeding data lakes, warehouses, and analytics platforms.
• Troubleshoot data processing, quality, and pipeline performance issues.
• Stay updated with emerging cloud data engineering technologies and best practices.
• Collaborate with data scientists, analysts, and stakeholders to deliver data solutions.
• Document data infrastructure, pipelines, and ETL processes for knowledge transfer.
• Create automated tests and integrate them into testing frameworks.

Skills

Python

SQL

PySpark

Databricks

REST APIs

CI/CD

GitHub

Data Warehousing

Big Data

Education

Bachelor's degree in Computer Science or Data Engineering

Master's degree preferred

Tools

Databricks

AWS

Azure

GCP

Redshift

BigQuery

Snowflake

CI/CD tooling

GitHub

Job description

JLL Technologies seeks a Data Engineer P2 to join the Enterprise Data team in a global, self-starting role. You will design and develop data solutions built on current technologies and patterns, collaborating with JLLT teams worldwide.

The role focuses on cloud data engineering, ETL, and data quality, partnering with data scientists and analysts to deliver scalable data infrastructure and pipelines.