# Databricks Data Architect

**Company:** [Unison Group](http://jobs.workable.com/companies/deKpPhPMtQZga7XoPG1tSo.md)
**Location:** Singapore, Singapore
**Workplace:** on site
**Employment type:** Full-time
**Department:** Bharat

[Apply for this job](http://jobs.workable.com/view/472314b6-ec05-4ee7-b5d9-5a06eed9f9cc)

## Description

-   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.

## 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)