Job Summary

We are seeking a highly skilled and hands-on Databricks Solution Architect with deep expertise in designing, building, and optimizing Lakehouse-based Data Engineering, Machine Learning, and AI solutions on the Databricks platform. This contract role is based in Singapore, offering flexibility to work on-site with enterprise clients or remotely within the region.

The ideal candidate will have strong experience in architecting modern data platforms, implementing scalable data pipelines, enabling MLOps, and delivering AI/LLM use cases using Databricks technologies. You will lead technical solution design, mentor engineering teams, and collaborate closely with business stakeholders to deliver secure, scalable, and high-performance data solutions.

Key Responsibilities

• Design, architect, and implement enterprise-scale Databricks Lakehouse solutions for analytics, data engineering, machine learning, and AI workloads.
• Develop scalable batch and streaming data pipelines using Delta Live Tables (DLT), Apache Spark, Spark SQL, Python, and Scala.
• Build, optimize, and maintain Delta Lake architectures for high-performance data processing.
• Configure and administer Databricks Workspaces, Unity Catalog, SQL Warehouses, clusters, and job orchestration.
• Design secure, governed, and compliant data platforms using Unity Catalog, access controls, and data governance best practices.
• Implement Infrastructure as Code (IaC) using Terraform and automate deployments through Git-based CI/CD pipelines.
• Optimize ETL, ELT, streaming, and machine learning workloads for performance, scalability, cost efficiency, and reliability.
• Build and operationalize MLOps pipelines using MLflow, model registry, experiment tracking, and automated model deployment.
• Develop AI and Generative AI solutions leveraging DBRX, Mosaic AI, Retrieval-Augmented Generation (RAG), and LLM frameworks.
• Integrate enterprise AI services such as OpenAI, Amazon Bedrock, Azure OpenAI, or Hugging Face where applicable.
• Implement monitoring, observability, and cost optimization using Splunk, Prometheus, CloudWatch, or similar monitoring platforms.
• Collaborate with cross-functional teams, business stakeholders, architects, and customers to define technical roadmaps and deliver enterprise data solutions.
• Provide technical leadership, conduct architecture reviews, and mentor engineering teams on Databricks and cloud best practices.

Required Skills & Experience

• 8+ years of experience in Data Engineering, Big Data, Cloud Data Platforms, or Analytics.

• 4+ years of hands-on experience with the Databricks platform.

• Strong expertise in:

• Databricks Workspaces

• Delta Lake

• Delta Live Tables (DLT)

• Unity Catalog

• SQL Warehouses

• Notebooks

• Job orchestration

• Strong programming skills in Python and/or Scala.

• Extensive experience with Apache Spark and Spark SQL.

• Strong knowledge of Lakehouse architecture, data modeling, ETL/ELT design, and streaming data processing.

• Experience implementing MLOps practices using MLflow, model lifecycle management, and automated deployment pipelines.

• Hands-on experience with Terraform, Git, Azure DevOps, GitHub Actions, or Jenkins.

• Strong understanding of cloud platforms:

• AWS

• Microsoft Azure

• Google Cloud Platform (GCP)

• Experience implementing security, governance, and data access controls using Unity Catalog.

• Experience with monitoring, logging, and observability tools.

• Strong problem-solving, communication, and stakeholder management skills.