AI Data Engineer – ML Data Pipelines

Job not on LinkedIn

🕒 6 days ago

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Empowers Staffing Inc

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2020

💼 Consulting

🎯 Recruiter

🤖 Artificial Intelligence

Consulting • Recruitment • Artificial Intelligence

Empowers Staffing Inc is presented in the provided content as (or appears to be represented by) a Minnesota-based IT consulting and staffing/ staff-augmentation provider. The webpage content largely corresponds to LAK Technology — a firm offering IT consulting and digital transformation services for small- to mid-size businesses, including software engineering (QA & testing, mobile apps, UI/UX, project management), data analytics and AI/ML, cloud migration and DevOps, custom solution development, and team extension/staff augmentation. The site lists a St. Paul, MN address and contact details and emphasizes workforce extension, bespoke software solutions, and data & cloud services.

📋 Description

• Design and build scalable data pipelines for ML workflows • Develop feature engineering and data preparation processes • Implement batch and real-time data ingestion systems • Ensure data quality, validation, and monitoring • Collaborate with ML engineers to support model training and deployment • Integrate pipelines with orchestration tools (Airflow or similar) • Optimize pipeline performance and cloud cost efficiency • Maintain documentation and version control of data workflows

🎯 Requirements

• 4+ years of experience in Data Engineering • Strong Python and SQL skills • Experience building data pipelines for ML or analytics systems • Hands-on experience with Spark, Databricks, or similar distributed processing frameworks • Experience with orchestration tools (Airflow or similar) • Experience in AWS, Azure, or GCP environments • Familiarity with data quality validation and monitoring frameworks • Understanding of feature engineering and model data lifecycle

🏖️ Benefits

• Remote work options