Azure Databricks Architect

Job Title: Azure Databricks Architect
Location : Iselin, NJ (Hybrid/Local candidates only)

The Databricks Architect is responsible for designing, implementing, and optimizing scalable data analytics and data engineering solutions on the Databricks Lakehouse Platform. This role requires deep expertise in cloud platforms (Azure/AWS/GCP), distributed data processing, Delta Lake architectures, and modern data engineering practices. The architect will collaborate with cross-functional teams to define data strategies, ensure platform reliability, and enable advanced analytics, ML, and BI use cases.

Key Responsibilities
Architecture & Design
Design end-to-end Databricks Lakehouse architectures for data ingestion, processing, storage, and consumption.
Define and implement Delta Lake patterns, including medallion architecture (Bronze/Silver/Gold).
Develop scalable data pipelines using PySpark, Spark SQL, and Databricks workflows.
Architect solutions for structured, semi-structured, and unstructured data.
Engineering & Implementation
Build robust ETL/ELT pipelines with Databricks notebooks, jobs, and workflows.
Design and implement high-performance streaming solutions using Structured Streaming.
Optimize Spark jobs for cost, performance, and scalability.
Implement CI/CD and automation using Databricks Repos, Git, and DevOps pipelines.
Cloud & Platform Expertise
Architect solutions across Azure/AWS/GCP leveraging native cloud services (e.g., Azure Data Factory, AWS Glue, GCP Dataflow).
Ensure security, governance, and compliance through Unity Catalog, RBAC, and encryption.
Monitor workloads and optimize cluster configurations for performance and cost.
Collaboration & Leadership
Work closely with data engineers, data scientists, BI teams, and business stakeholders.
Act as a subject matter expert (SME) for Databricks best practices, standards, and patterns.
Conduct architectural reviews and guide teams on design decisions.
Lead PoCs, evaluate new features, and drive platform adoption.
Quality, Governance & Observability
Define standards for data quality, lineage, observability, and governance.
Implement automated testing frameworks for pipelines and notebooks.
Establish performance baselines and monitoring dashboards.