Data Architect (Databricks) - GLOBAL IT CON LLC

Job Title Data Architect (Databricks)

Location Remote

Duration:- Long term

NOTE: Databricks certification is required

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

• 15+ 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.

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