Remote Data & AI Engineer — Lakehouse & MLOps

RevStar; Portland (ME); 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 Activities

Collaborative, Innovative Culture

Job summary

RevStar is seeking a Data & AI Engineer to design and implement scalable Lakehouse pipelines and production-grade AI workflows across AWS, Azure, and GCP. You will partner with data scientists, engineers, and client leaders to deliver cloud-native data solutions that operationalize AI at scale.

The role emphasizes Databricks, MLflow, and CI/CD integration, with a remote-first policy and opportunities to influence architecture across client engagements.

Qualifications

• 3+ years in cloud-native data engineering with 2+ years of dedicated, hands‑on Databricks, Apache 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 certification is strongly preferred and will set your application apart.
• Proven track record operating in fast‑paced Agile/DevOps consulting environments, delivering cloud-agnostic architectures with exceptional client communication.

Responsibilities

• Design, build, and optimize scalable ETL/ELT pipelines across multi-cloud environments.
• Collaborate with ML engineers to operationalize AI models and track via MLflow.
• Implement robust CI/CD pipelines and Infrastructure-as-Code to enable production-grade deployments.
• Enforce data governance and translate complex AI data requirements into clear business outcomes.

Skills

Python

SQL

Spark

MLflow

Databricks

CI/CD

Tools

Databricks CLI

Terraform

Job description

RevStar is seeking a Data & AI Engineer to design and implement scalable Lakehouse pipelines and production-grade AI workflows across AWS, Azure, and GCP. You will partner with data scientists, engineers, and client leaders to deliver cloud-native data solutions that operationalize AI at scale.

The role emphasizes Databricks, MLflow, and CI/CD integration, with a remote-first policy and opportunities to influence architecture across client engagements.