Databrick Architect

Role : Databrick Architect

Location : Los Angeles CA or New York NY (onsite)

Type: C2C

Job Description:

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.

Required Skills & Experience

Technical Skills

7 years of experience in data engineering/architecture.

3 years of hands-on experience with Databricks.

Strong expertise in Spark PySpark SQL and distributed data processing.

Deep understanding of Delta Lake features: ACID transactions OPTIMIZE ZORDER Auto Loader.

Experience with workflow orchestration jobs and Databricks REST APIs.

Hands-on expertise with at least one cloud platform:

o Azure (preferred): ADF ADLS Key Vault Event Hub Azure DevOps

o AWS: S3 Glue Lambda Kinesis

o GCP: GCS Dataflow Pub/Sub

Familiarity with CI/CD Git DevOps and Infrastructure-as-Code (Terraform preferred).

Soft Skills

Strong analytical and problem-solving skills.

Excellent communication and stakeholder management.

Ability to lead design discussions and guide technical teams.

Strong documentation and architectural blueprinting skills.

Preferred Qualifications

Databricks certifications such as:

o Databricks Certified Data Engineer Professional

o Databricks Certified Machine Learning Professional

o Databricks Lakehouse Fundamentals

Experience with MLflow Feature Store or MLOps workflows.

Experience working in regulated industries (BFSI healthcare etc.).