Senior Data Engineer — Cloud Data Pipelines & Databricks

Jones Lang LaSalle Incorporated; Guadalajara; On-site; MXN 1,100,000 - 1,500,000; Full time

Job summary

Jones Lang LaSalle Incorporated (JLL) is seeking a Senior Data Engineer to design and implement scalable, cloud-based data infrastructure. In this global role, you will partner with the Enterprise Data team across country, regional, and global levels to deliver robust data pipelines and data quality controls.

You will work with Python, SQL, PySpark, and Databricks, exploring data sources, building ingestion architectures, and ensuring reliable data flows into lakes and warehouses.

Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, or a related field (Master’s degree preferred)
• Minimum 5+ years of experience in data engineering or full-stack development, with a focus on cloud-based environments
• Strong Databricks experience
• Advanced database/backend testing with complex SQL queries for data validation

Responsibilities

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

Skills

Python

SQL

PySpark

Databricks

REST

CI/CD

Cloud platforms

Data testing

Education

Bachelor’s degree in Computer Science or related

Master’s degree preferred

Tools

AWS

Azure

GCP

Snowflake

Redshift

GitHub

Job description

Jones Lang LaSalle Incorporated (JLL) is seeking a Senior Data Engineer to design and implement scalable, cloud-based data infrastructure. In this global role, you will partner with the Enterprise Data team across country, regional, and global levels to deliver robust data pipelines and data quality controls.

You will work with Python, SQL, PySpark, and Databricks, exploring data sources, building ingestion architectures, and ensuring reliable data flows into lakes and warehouses.