Databricks Engineer

About the role

Build what matters as a Databricks Engineer at RevStar. RevStar is a Databricks Partner launching a cloud-agnostic practice focused on Data, Machine Learning (ML), and Artificial Intelligence (AI) services. Our mission is to help businesses modernize their data platforms, optimize analytics workflows, and implement scalable AI-driven solutions using Databricks. We are passionate about what we build and how we build it. From architecture and design to coding and delivery, we approach each project with an agile mindset, continuously analyzing goals and business needs to ensure optimal outcomes. At RevStar, we foster a collaborative, remote-first culture where teams freely share ideas, innovate together, and grow both individually and collectively. By joining us, you'll have the opportunity to work with cutting-edge technologies across diverse industries, delivering value-driven products for clients who prioritize quality and performance. We believe in the pursuit of better, not just in cloud-native app development, but in creating meaningful experiences and outcomes that matter. We are seeking a highly skilled Databricks Engineer to join our team. This role will be hands-on, working closely with architects, data scientists, and customers to build, optimize, and deploy high-performance data and AI solutions. As a Databricks Engineer, you will be responsible for building and optimizing data pipelines, implementing data processing frameworks, and enabling AI/ML solutions within Databricks. You will work across data ingestion, transformation, and orchestration while ensuring scalability, performance, and security. This is a technical hands-on role, requiring expertise in Apache Spark, Delta Lake, and MLOps, as well as experience working with large-scale data architectures. You will collaborate with architects and business stakeholders to ensure solutions align with customer needs and best practices. 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. Key Responsibilities Data Engineering & Pipeline Development Develop and optimize data pipelines using Apache Spark and Delta Lake within Databricks. Implement ETL/ELT workflows, ensuring efficient data ingestion, transformation, and storage. Design Lakehouse architecture-based solutions that scale across structured and unstructured data sources. Integrate Databricks with cloud storage solutions (Azure Data Lake, AWS S3, Google Cloud Storage) for seamless data management. Performance Optimization & Automation Optimize Spark jobs for scalability, cost efficiency, and low latency. Implement monitoring and alerting solutions to track job performance and detect failures. Develop automated data validation, testing, and quality assurance processes. Management AI/ML Integration & MLOps Support Support ML model training and deployment within Databricks, integrating with MLflow for experiment tracking and model versioning. Collaborate with data scientists and ML engineers to enable scalable AI solutions. Implement feature engineering pipelines and integrate models into production environments. Security, Governance & Best Practices Ensure data security, access control, and compliance with industry standards (GDPR, HIPAA, SOC 2, etc.). Follow Databricks best practices for data lineage, governance, and metadata management. Document processes, configurations, and best practices for internal and client use. Must-Have: 3+ years of hands-on experience in data engineering, with a focus on big data processing and cloud-native architectures. 2+ years of hands-on experience with Databricks, including Apache Spark, Delta Lake, and MLflow. Databricks Certifications (Mandatory): Databricks Certified Data Engineer Associate (or higher) Proficiency in Python, SQL, and Spark-based frameworks. Experience in developing and optimizing large-scale ETL/ELT pipelines. Strong understanding of Lakehouse architecture and cloud-agnostic data solutions. Familiarity with CI/CD pipelines and Infrastructure-as-Code (IaC) for Databricks (e.g., Terraform, Databricks CLI). Knowledge of data governance, security, and compliance best practices. Experience working in Agile development environments, following DevOps/MLOps best practices. Nice-to-Have: Additional Databricks Certifications (e.g., Databricks Certified Machine Learning Associate). Experience with real-time streaming solutions (e.g., Kafka, Kinesis, Event Hub). Familiarity with cloud storage and orchestration tools (e.g., Apache Airflow, Prefect). Background in AI/ML integration within Databricks, assisting in feature engineering and model deployment. Experience working in client-facing roles or consulting environments. Benefits for Full-Time W2 Positions: Paid Time Off - Take the time you need to recharge and stay productive. Remote-First Working Environment - Collaborate from

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