Remote Databricks Data & AI Engineer - Lakehouse

RevStar Consulting Inc; New York (NY); Remote; USD 140,000 - 190,000; 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 Consulting Inc is seeking a Data & AI Engineer (Databricks Specialist) to design and deploy scalable data lakehouse solutions in a remote US-based team. You will collaborate with data architects, scientists, and client leaders to turn complex data into production-ready AI models across multi-cloud environments.

You will own ETL/ELT pipelines, optimize Spark workloads, and integrate MLflow for model deployment, with CI/CD and IaC to ensure robust, repeatable deployments in enterprise

Qualifications

• 3+ years in cloud-native data engineering with 2+ years in Databricks/Spark/Delta Lake/MLflow.
• Strong Python/SQL and Spark framework proficiency, plus CI/CD & IaC.
• Databricks certifications (Associate/Professional) preferred, or Databricks ML Professional.

Responsibilities

• Design, build, and optimize scalable ETL/ELT pipelines across multi-clouds using Spark and Delta Lake.
• Operationalize AI models in Databricks with MLflow, including CI/CD pipelines and IaC deployments.
• Enforce data security, governance, and collaborate with client leaders to translate data needs into concrete outcomes.

Skills

Python

SQL

Spark

CI/CD

Infrastructure as Code

Tools

Databricks

Apache Spark

Delta Lake

MLflow

Terraform

Databricks CLI

Job description

RevStar Consulting Inc is seeking a Data & AI Engineer (Databricks Specialist) to design and deploy scalable data lakehouse solutions in a remote US-based team. You will collaborate with data architects, scientists, and client leaders to turn complex data into production-ready AI models across multi-cloud environments.

You will own ETL/ELT pipelines, optimize Spark workloads, and integrate MLflow for model deployment, with CI/CD and IaC to ensure robust, repeatable deployments in enterprise