TL;DR

Databricks Engineer (Data Science/ML): Design, build, and maintain data pipelines and ETL processes using Databricks and Apache Spark with an accent on optimizing data workflows for performance and scalability. Focus on implementing data lakehouse architecture and ensuring data quality, governance, and security across all data assets.

Location: Guadalajara

Company

%hirify_global% is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology.

What you will do

Design, build, and maintain data pipelines and ETL processes using Databricks and Apache Spark.
Optimize data workflows for performance, scalability, and cost efficiency.
Implement data lakehouse architecture and manage data ingestion from multiple sources.
Collaborate with data scientists and analysts to enable advanced analytics and machine learning workloads.
Ensure data quality, governance, and security across all data assets.
Monitor and troubleshoot Databricks clusters, jobs, and workflows.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, or related field.
Good experience in data engineering or big data technologies using Databricks and PySpark.
Strong knowledge of SQL, Python, and data modeling principles.
Experience managing Data Ingestion Pipelines.
Experience automating tests for pipelines/notebooks.
Experience with cloud platforms (AWS, Azure, or GCP) and their data services.
Proficient English (reading, writing, and technical communication).

Nice to have

Familiarity with Delta Lake, Lakehouse architecture, and data governance.
Understanding of CI/CD pipelines and DevOps practices for data workflows.

Culture & Benefits

Encourages flexibility in how, when, and where people get their work done, allowing a better work-life balance, and greater empowerment.
Creates opportunities to achieve valuable change.
Make it easy for you to deepen knowledge and learn new skills while you’re still doing the day job.

Databricks Engineer (Data Engineering)