Databricks Data Architect
Salary not listed
Unison Group · Charlotte, NC · Full-time
Build · Found · posted 20 days ago
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Experience asked for: not stated
This posting never puts a number on it, or names several that contradict each other. Read the requirements below before you rule yourself out — we would rather say nothing than guess at it.
• We're seeking a hands-on experience in Databricks with deep technical expertise in building and optimizing Lakehouse-based data and AI solutions.
• In this role, you'll design, develop, and operationalize Delta Lakehouse architectures using Databricks, driving real-world outcomes for enterprise customers. You'll take ownership of implementation tasks, lead technical delivery, and mentor engineering teams in best practices across data engineering, governance, and AI
Key Responsibilities
• Design and implement scalable data pipelines using Delta Live Tables (DLT), Spark SQL, Python, or Scala
• Optimize ETL, streaming, and ML workloads for performance, cost efficiency, and reliability
• Administer and configure Databricks Workspaces, Unity Catalog, and cluster policies for secure, governed environments
• Automate infrastructure and deployments using Terraform, Git, and CI/CD pipelines
• Implement observability, cost optimization, and monitoring frameworks using tools like Splunk, Prometheus, or CloudWatch
• Collaborate with customers to build AI and LLM solutions leveraging MLflow, DBRX, and Mosaic AI
Work Location: Singapore
Requirements
Required Skills & Experience
• Strong hands-on experience with Databricks, including workspace setup, notebooks, clusters, and job orchestration
• Expertise in Delta Lake, DLT, Unity Catalog, and SQL Warehouses
• Proficiency in Python or Scala for data engineering and ML workflows
• Strong understanding of AWS, Azure, or GCP cloud ecosystems
• Experience with Terraform automation, DevOps, and MLOps practices
• Familiarity with monitoring and governance frameworks for large-scale data platforms
Good to Have Skills:
• Machine Learning, Deep Learning, NLP, or Generative AI
• Designing distributed and scalable systems
• API-first and microservices architecture
• Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• MLOps tools (MLflow, Kubeflow, SageMaker, etc.)
• Data platforms (Spark, Databricks, Snowflake)