Data & AI Engineer: Lakehouse & MLOps on Multi-Cloud

RevStar Consulting Inc; New Jersey; Remote; USD 120,000 - 160,000; 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…

Collaborative, Innovative Culture

Job summary

RevStar Consulting Inc. seeks a Data & AI Engineer to design and deliver production-grade Lakehouse solutions across cloud platforms. You’ll partner with data scientists and engineers to turn complex data into scalable AI models and reliable data pipelines.

You will own end-to-end data engineering initiatives, implement MLflow-based model deployment, and ensure governance and security while working in a remote-first, fast-paced consulting environment.

Qualifications

• 3+ years in cloud-native data engineering with 2+ years on Databricks, Spark, Delta Lake, and MLflow.
• Proficiency in Python, SQL, Spark, CI/CD, and IaC via Terraform or Databricks CLI.
• Databricks Certified Data Engineer (Associate/Professional) or Databricks ML Professional preferred.
• Agile/DevOps consulting experience with cloud-agnostic architectures and strong client communication.

Responsibilities

• Design, build, and optimize ETL/ELT pipelines across multi-cloud using Spark and Delta Lake.
• Operationalize AI models in Databricks with MLflow; implement CI/CD and IaC.
• Enforce data security, governance, and partner with client leaders to translate requirements into outcomes.

Skills

Databricks

Apache Spark

Delta Lake

MLflow

Python

SQL

CI/CD

Terraform

Databricks CLI

Databricks Certified

Job description

RevStar Consulting Inc. seeks a Data & AI Engineer to design and deliver production-grade Lakehouse solutions across cloud platforms. You’ll partner with data scientists and engineers to turn complex data into scalable AI models and reliable data pipelines.

You will own end-to-end data engineering initiatives, implement MLflow-based model deployment, and ensure governance and security while working in a remote-first, fast-paced consulting environment.