Remote Data & AI Engineer: Databricks & ML Ops

RevStar Consulting Inc; Florida; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Paid Time Off

Remote-First Working Environment

Comprehensive Health Coverage

401(k) Retirement Plan

Annual Learning & Development Stipend

Peer Mentorship & Coaching

Professional Growth Opportunities

Company Outings & Volunteer Opportuni

Job summary

RevStar Consulting Inc in the United States is seeking a Data & AI Engineer to design, build, and optimize cloud-native data pipelines and AI workflows. You will work with data architects, scientists, and client leaders to deliver scalable Lakehouse solutions using Databricks, Spark, Delta Lake, and MLflow in multi-cloud environments.

The role emphasizes fast-paced execution, production-ready deployments, and strong collaboration across engineering and product teams.

Qualifications

• 3+ years of cloud-native data engineering experience.
• 2+ years with Databricks, Apache Spark, Delta Lake, and MLflow.
• Proficiency in Python and SQL, with Spark frameworks and CI/CD.
• Ability to implement Infrastructure-as-Code (Terraform or Databricks CLI).
• Databricks Certification strongly preferred.
• Experience in fast-paced Agile/DevOps consulting environments.

Responsibilities

• Design, build, and optimize scalable ETL/ELT pipelines in multi-cloud environments.
• Operationalize AI models with MLflow and ensure production-grade deployments.
• Collaborate with client leaders to translate data needs into actionable outcomes.
• Develop robust data governance and documentation practices.

Skills

Databricks

Apache Spark

Delta Lake

MLflow

Python

SQL

CI/CD

Terraform

Databricks CLI

Tools

Terraform

Databricks CLI

MLflow

Job description

RevStar Consulting Inc in the United States is seeking a Data & AI Engineer to design, build, and optimize cloud-native data pipelines and AI workflows. You will work with data architects, scientists, and client leaders to deliver scalable Lakehouse solutions using Databricks, Spark, Delta Lake, and MLflow in multi-cloud environments.

The role emphasizes fast-paced execution, production-ready deployments, and strong collaboration across engineering and product teams.