• Design, develop, and deploy scalable applications in our AWS environment, ensuring the implementations meet business requirements and adhere to high-quality standards. • Collaborate closely with cross-functional teams, including product managers, designers, and fellow engineers, driving seamless product development from concept to deployment. • Keep our tech stack modern and competitive by staying current with the latest industry trends and technologies. • Enhance system observability and reliability through writing tests, adding key metrics, debugging issues, and learning from incidents. • Contribute to our team's growth by assisting with resume screening and conducting interviews for new talent.

• Develop and maintain full stack web applications using Python and React. • Design and build scalable cloud-native solutions using modern architecture and best practices. • Develop REST APIs and integrate third-party services. • Deploy and manage applications on AWS, Azure, or Google Cloud Platform (GCP). • Implement containerized applications using Docker and Kubernetes. • Build and maintain CI/CD pipelines and Infrastructure as Code (IaC). • Optimize applications for performance, scalability, security, and reliability. • Collaborate with technical and business teams to deliver project requirements. • Perform testing, debugging, code reviews, and maintain technical documentation. • Stay up to date with emerging technologies and recommend improvements when appropriate.

Role Description 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. Qualifications - 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 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. Requirements - 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. Company Description

• You work with multiple clients as the main technical voice for Databricks. • You lead your customers on a transformational journey, helping them to evaluate and adopt Databricks as part of their strategy • You implement the technical strategy in the accounts with a clear understanding of this strategy and its business value. • You build a movement of technical champions within the account. • You align technical strategies around Databricks solutions. • You provide structured mentorship for other team members. • Gain your peers' respect through your experience, insight, and contributions.