**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.

• As part of our engineering team, you ensure that critical infrastructures remain secure and operational. • You implement modern IAM solutions in complex cloud environments, combining highest security standards with technological innovation. • Build and configure IAM/PAM solutions (e.g., Keycloak, Zitadel), including role models, RBAC/ABAC and provisioning • Integrate applications via OAuth2, OIDC, SAML or SCIM – including testing and troubleshooting • Implement JIT/JEA concepts and minimal standing privileges • Implement role/right workflows, attestations and recertifications • Technical implementation of policies using Terraform, OPA or comparable tools • Integrate IAM into build and deployment pipelines (e.g., GitLab CI/CD, Jenkins) • Connect cloud services and container platforms (e.g., Kubernetes) to central identity services • Technically migrate existing AD/IAM structures to modern cloud or private-cloud environments • Analyze authentication flows, perform security hardening and ensure auditability

• Perform forestry analyses, generate reports, and validate results to support carbon project certification • Review and challenge forest-related data and assumptions provided by project developers • Ensure scientific rigour and consistency across projects, particularly within tropical forest ecosystems • Contribute to the documentation of methodologies and build internal knowledge on forestry topics • Provide forestry expertise in internal discussions and occasional calls with external stakeholders • Support the evolution of methodologies by investigating and proposing improvements (e.g. carbon pools, stratification, biomass estimation) • Contribute to the development of new forest science modules • Stay up to date with relevant scientific literature and IPCC guidance

• Installation, configuration and testing of Genesys software • Support large implementations of the Genesys product suite • Daily operations and maintenance