Overview
Skills
Job Details
Job Title Data Architect (Databricks)
Location Remote
Duration: Full-time
Role: We are seeking a seasoned Data Architect with deep expertise in Databricks, Lakehouse architecture, and AI/ML/GenAI enablement to lead a critical modernization initiative. The role involves transforming a legacy platform into a future-ready, scalable, cloud-native Databricks-based architecture. You will drive design and implementation of high-performance data pipelines, orchestrate data workflows, and integrate AI/ML capabilities across the stack to unlock real-time intelligence and innovation.
Key Responsibilities
- Lead the architectural modernization from an on-prem/legacy platform to a unified Databricks Lakehouse ecosystem.
- Architect and optimize data pipelines (batch and streaming) to support AI/ML and GenAI workloads on
Databricks.
- Migrate and re-engineer existing Spark workloads to leverage Delta Lake, Unity Catalog, and advanced
performance tuning in Databricks.
- Drive integration of AI/ML models (including GenAI use cases) into operational data pipelines for real-time
decision-making.
- Design and implement robust orchestration using Apache Airflow or Databricks Workflows, with CI/CD
integration.
- Establish data governance, security, and quality frameworks aligned with Unity Catalog and enterprise
standards.
- Collaborate with data scientists, ML engineers, DevOps, and business teams to enable scalable and governed
AI solutions.
Required Skills
- 12+ years in data engineering or architecture, with a strong focus on Databricks (at least 4-5 years) and AI/ML
enablement.
- Deep hands-on experience with Apache Spark, Databricks (Azure/AWS), and Delta Lake.
- Proficiency in AI/ML pipeline integration using Databricks MLflow or custom model deployment strategies.
- Strong knowledge of Apache Airflow, Databricks Jobs, and cloud-native orchestration patterns.
- Experience with structured streaming, Kafka, and real-time analytics frameworks.
- Proven ability to design and implement cloud-native data architectures.
- Solid understanding of data modeling, Lakehouse design principles, and lineage/tracking with Unity Catalog.
- Excellent communication and stakeholder engagement skills.
Preferred Qualifications
- Certification in Databricks Data Engineering Professional is highly desirable.
- Experience transitioning from in house data platforms to Databricks or cloud-native environments.
- Hands-on experience with Delta Lake, Unity Catalog, and performance tuning in Databricks.
- Expertise in Apache Airflow DAG design, dynamic workflows, and production troubleshooting.
- Experience with CI/CD pipelines, Infrastructure-as-Code (Terraform, ARM templates), and DevOps practices.
- Exposure to AI/ML model integration within real-time or batch data pipelines.
- Exposure to MLOps, MLflow, Feature Store, and model monitoring in production environments.
- Experience with LLM/GenAI enablement, vectorized data, embedding storage, and integration with Databricks is an added advantage.