Databricks Solution Architect

Job Description

We’re seeking a hands-on Senior Consultant – Databricks with deep technical expertise in building and optimizing Lakehouse-based data and AI solutions. This is a contract role based in Singapore, offering flexibility to work on-site with clients or remotely within the region.

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.

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.

Nice to Have

• Experience developing AI/LLM pipelines and RAG architectures on Databricks.
• Exposure to Bedrock, OpenAI, or Hugging Face integrations.
• Databricks certifications (Data Engineer, Machine Learning, or Solutions Architect) preferred.