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.