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Data & AI Engineer

Rev Star•United States•🌍 Remote
Full-timeSenior
👁️ 0 views•📝 0 applications•Posted 10/9/2026•Expires 12/8/2026
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Job Description

Build What Matters at RevStar Role Title: Data & AI Engineer (Databricks Specialist) Reports To: Data & AI Practice Lead Location: Remote (US-Based / Eastern or Central Time Zone Preferred) Employment Type: Full-Time W2 Ready to build greenfield Lakehouse solutions at the bleeding edge of AI and big data?

RevStar is an innovation shop and official Databricks Partner launching a dedicated, cloud-agnostic Data, ML, and AI practice. Tired of spending months in endless corporate approval loops only to build AI concepts that never reach production?

We turn modern Lakehouse architectures and production AI into live, scalable enterprise solutions in fast-paced execution cycles. We build fast, cut bureaucratic red tape, and ship production-ready solutions for client founders and tech leaders who value real engineering impact.

In this role, you will work directly with data architects, scientists, and client leaders to turn complex data into scalable, production-ready AI models. Above all, our team operates on three core principles: Self-Mastery: We hold a high bar for how we think, communicate, and improve.

Ownership: We own outcomes, not just effort. Shared Destiny: We rise or fall together. Your Impact Pillars 1. Databricks Lakehouse & Pipeline Engineering: Design, build, and optimize scalable ETL/ELT pipelines using Apache Spark and Delta Lake across multi-cloud environments (AWS, Azure, GCP).

Implement robust Lakehouse architectures that seamlessly process both structured and unstructured data at enterprise scale. Automate data ingestion, storage, and feature engineering workflows to support downstream analytics and real-time AI workloads. 2.

Performance Tuning & MLOps Integration: Fine-tune Spark jobs for low latency, high throughput, and maximum cloud cost-efficiency across client environments. Partner with ML engineers and data scientists to operationalize AI models inside Databricks using MLflow for tracking, versioning, and deployment.

Implement robust CI/CD pipelin

Required Skills

DatabricksApache SparkDelta LakeETLELTAWSAzureGCPMLflowCI/CD

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