Data Architect (Databricks) - DRC Systems USA LLC

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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