Job description
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.
This job post has been translated by AI and may contain minor differences or errors.
