Databricks Engineer

- Reports To: Data & AI Practice Lead
- Location: Remote (US-Based / Eastern or Central Time Zone Preferred)
- Employment Type: Contract

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. We are seeking a high-caliber Databricks Engineer to build, optimize, and deploy high-performance data pipelines and MLOps frameworks for enterprise clients across AWS, Azure, and GCP. 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, the ideal candidate embodies RevStar’s core values:

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

Your technical contributions are organized into four strategic pillars:

1. Lakehouse Architecture & Pipeline Engineering
- Design, build, and optimize scalable ETL/ELT pipelines using Apache Spark and Delta Lake across multi-cloud environments (AWS S3, Azure Data Lake, GCS).
- Implement robust Lakehouse architectures that seamlessly process both structured and unstructured data at enterprise scale.
- Automate data ingestion and storage workflows to support downstream analytics and real-time operational reporting.

2. Performance Optimization & Automation
- Fine-tune Spark jobs for low latency, high throughput, and maximum cloud cost-efficiency.
- Implement CI/CD pipelines and Infrastructure-as-Code (Terraform, Databricks CLI) for automated deployments.
- Build automated monitoring, alerting, and data quality validation frameworks to guarantee pipeline reliability.

3. MLOps & AI Integration
- Partner with ML engineers and data scientists to build production-grade feature engineering pipelines.
- Support model training, tracking, versioning, and deployment inside Databricks using MLflow.
- Operationalize AI/ML models into secure, scalable production environments for client applications.

4. Data Governance & Client Excellence
- Enforce enterprise data security, access controls, and compliance standards (GDPR, HIPAA, SOC 2).
- Establish best practices for data lineage, metadata management, and operational documentation.
- Collaborate with client-facing stakeholders to align technical implementations with critical business outcomes.