Data Engineer

v4c.ai

. Architect, build, and maintain scalable data pipelines for AI/ML workflows, including feature engineering and model-training datasets.

Posted 11/10/2026full-timeRemote • MexicoJuniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and maintaining scalable data pipelines for AI/ML workflows, with proficiency in Databricks, Python, and data governance frameworks. Capable of collaborating with data scientists to operationalize machine learning models while ensuring compliance with industry standards.

Highest-signal resume keywords

Databricks Platform EngineeringData Pipeline DevelopmentPython ProgrammingApache SparkData Governance Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringETL ProcessesFeature EngineeringModel-Training DatasetsDelta LakeMachine Learning FrameworksMLOps PracticesData WarehousingDatabricks APIsScala

Soft Skills

Interpersonal SkillsCommunication SkillsCollaboration

Tools & Technologies

DatabricksAWSAzureGCPTensorFlowPyTorchScikit-learn

Certifications & Qualifications

Databricks Certified Professional

Industry Keywords

AI/ML WorkflowsData LakesData GovernanceCost-EffectivenessResource Utilization

Tech Stack

Tools & technologies

ApacheAWSAzureETLGoogle Cloud PlatformJavaPythonPyTorchScalaScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Architect, build, and maintain scalable data pipelines for AI/ML workflows, including feature engineering and model-training datasets.
Design and implement data pipelines, data lakes, and data warehousing solutions.
Partner with data scientists and analysts to deploy machine learning models and analytics solutions on Databricks.
Enhance Databricks cluster performance for cost-effectiveness and efficient resource utilization.
Implement Databricks security features, enforce data governance, and maintain compliance with industry standards.
Collaborate with data scientists to securely deploy and operationalize machine learning models in production.
Contribute to the technical direction and best practices of data engineering initiatives.
Stay current with Databricks innovations, best practices, and industry trends, and share knowledge across the team.

Requirements

What you’ll need

1-4 years in data engineering or related fields.
Solid experience with Databricks, focusing on platform engineering, data engineering, and analytics.
Proficiency in Python, Scala, or Java.
Strong experience with Databricks APIs, Apache Spark, and Delta Lake.
Solid experience in data warehousing, ETL processes, and data governance frameworks.
Practical experience supporting AI/ML lifecycles, including familiarity with ML frameworks such as TensorFlow or PyTorch, or MLOps practices.
Proven ability to work independently, take ownership of projects, and collaborate effectively with a team.
Excellent interpersonal and communication skills for effective collaboration with stakeholders.
Hands-on experience with AWS, Azure, or GCP is desirable.
Databricks Certified Professional or equivalent certification is desirable.
Familiarity with ML concepts and libraries such as TensorFlow, PyTorch, and Scikit-learn is desirable.

Benefits

Comp & perks

Best-in-industry learning and growth opportunities.
Comprehensive Health and wellness benefits, including medical insurance for self and family.
Employee rewards program.
Paid parental leave.
Flexible schedule.
Remote working benefits.