Databricks Architect

About the Team

Join a forward-thinking team dedicated to building and scaling an innovative, ephemeral, and immutable Data and Machine Learning platform on AWS. We utilize modern technologies like Databricks, Terraform, and Python to develop a fully automated environment that drives data analytics and AI innovation throughout the organization.

About the Role

We are looking for a skilled, hands-on Databricks Architect to act as our top technical expert and lead practitioner for the Databricks Platform. This role combines architectural leadership with practical implementation. You will be responsible not only for defining the architectural vision but also for developing core, reusable patterns and reference architectures that will accelerate our teams. The ideal candidate is a master of the Databricks ecosystem who leads by example, demonstrates what's possible through hands-on development, and empowers teams to build robust, scalable data solutions.

What You'll Do

• Develop and Implement Reference Architectures: Design, build, and maintain a library of production-quality reference architectures and reusable patterns that showcase best practices and accelerate development for engineering teams.
• Architect and Prototype Solutions: Architect and build proofs-of-concept for end-to-end solutions on the Databricks Lakehouse Platform, actively demonstrating feasibility and validating complex designs through hands-on implementation.
• Advise Through Doing: Serve as the primary consultant for engineering teams on all aspects of Databricks, providing expert guidance that extends beyond diagrams to include code, best practices, and hands-on support.
• Lead Platform Training: Create training sessions and train engineers, leading the adoption and implementation of new features such as Unity Catalog, Delta Live Tables, and advanced MLOps capabilities.
• Establish and Govern Best Practices: Define, document, and evangelize standards for Databricks development, including data modeling, performance tuning, security, and cost management.
• Mentor and Coach: Mentor engineers and other technical staff through code reviews, paired programming, and design sessions, elevating the overall technical proficiency of the organization within the Databricks ecosystem.

What You'll Bring

Core Qualifications:

• A bachelor s degree in Computer Science or a related field, with over 10 years of experience in data engineering, data warehousing, or software engineering, including significant experience in an architect role.
• Proven experience acting as a hands-on technical architect and advisor on large-scale data projects.
• Excellent communication and interpersonal skills, with the ability to influence and guide technical teams and stakeholders effectively.
• A strategic mindset with a passion for solving complex data challenges and driving business outcomes through technology.
• The ability to think critically, challenge assumptions, and make clear, well-reasoned architectural decisions.

Technical Expertise: MUST HAVE !!!

• Databricks Mastery: Deep, expert-level knowledge of the Databricks Platform, including:

• Unity Catalog: Designing and implementing data governance and security.
• Delta Lake & Delta Live Tables: Architecting and building reliable, scalable data pipelines.
• Performance & Cost Optimization: Expertise in tuning Spark jobs, optimizing cluster usage, and managing platform costs.
• MLOps: Strong, practical understanding of the machine learning lifecycle on Databricks using tools like MLflow.
• Databricks SQL: Knowledge of designing and optimizing analytical workloads.
• Mosaic AI: Knowledge of designing and optimizing AI Agents.

• Cloud & Infrastructure: Deep knowledge of cloud architecture and services on AWS. Strong command of Infrastructure as Code (Terraform, YAML).
• Data Engineering & Programming: Strong background in data modeling, ETL/ELT development, and advanced, hands-on programming skills in Python and SQL.
• CI/CD & Automation: Experience with designing and implementing CI/CD pipelines (preferably with GitHub Actions) for data and ML workloads.
• Observability: Familiarity with implementing monitoring, logging, and alerting for data platforms.
• Automation: The platform is ephemeral, and all changes are implemented using Terraform and Python. Expertise in Terraform and Python is a must