Remote Data & AI Engineer: Lakehouse & MLOps

RevStar; Yorktown (VA); Remote; USD 120,000 - 170,000; 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 Activities

Collaborative, Innovative Culture

Job summary

RevStar is seeking a Data & AI Engineer to design and build scalable Lakehouse architectures, working with data architects, scientists, and client leaders to deliver production-ready AI models.

In this remote-friendly role, you will own data pipelines, implement MLflow-based workflows, and collaborate across multi-cloud environments (AWS, Azure, GCP) while embracing a culture of ownership and continuous improvement.

Qualifications

• 3+ years in cloud-native data engineering with 2+ years Databricks, Spark, Delta Lake, and MLflow experience.
• High proficiency in Python, SQL, Spark frameworks, CI/CD, and Infrastructure-as-Code (Terraform or Databricks CLI).
• Active Databricks Certified Data Engineer (Associate or Professional) or Databricks ML Professional preferred.
• Consulting mindset with experience in fast-paced Agile/DevOps environments and cloud-agnostic architectures.

Responsibilities

• Design, build, and optimize scalable ETL/ELT pipelines across multi-cloud environments.
• Operationalize AI models inside Databricks using MLflow; implement CI/CD and IaC for production deployments.
• Partner with data architects and client leaders to translate data needs into scalable, governed solutions.

Skills

Cloud data engineering

Databricks

Python

SQL

CI/CD

Infrastructure as Code

Tools

Databricks

Apache Spark

Delta Lake

MLflow

Terraform

Databricks CLI

Job description

RevStar is seeking a Data & AI Engineer to design and build scalable Lakehouse architectures, working with data architects, scientists, and client leaders to deliver production-ready AI models.

In this remote-friendly role, you will own data pipelines, implement MLflow-based workflows, and collaborate across multi-cloud environments (AWS, Azure, GCP) while embracing a culture of ownership and continuous improvement.