Data & AI Engineer (Databricks Specialist)

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 pipelines and Infrastructure-as-Code (Terraform, Databricks CLI) to establish automated, production-grade deployments.

3. Data Governance & Client Leadership: Enforce enterprise data security, access controls, and compliance standards (GDPR, HIPAA, SOC 2) within Unity Catalog and cloud ecosystems. Collaborate directly with client tech leaders, product managers, and engineering teams to translate complex AI data requirements into clear business outcomes. Author clean technical documentation and lead seamless solution handoffs to client engineering teams upon project completion.

Requirements

What You Bring

• Core Experience: 3+ years in cloud-native data engineering with 2+ years of dedicated, hands-on Databricks, Apache Spark, Delta Lake, and MLflow experience.
• Technical Mastery: High proficiency in Python, SQL, Spark frameworks, CI/CD, and Infrastructure-as-Code (Terraform or Databricks CLI).
• Strong Differentiator: Active Databricks Certified Data Engineer (Associate or Professional) or Databricks ML Professional certification is strongly preferred and will set your application apart.
• Consulting Mindset: Proven track record operating in fast-paced Agile/DevOps consulting environments, delivering cloud-agnostic architectures with exceptional client communication.