Databricks Architect

Responsibilities

Define and drive enterprise-scale data architecture and modernization strategies.

Design end-to-end cloud data platforms using Databricks, Snowflake, and modern data engineering frameworks.

Architect scalable batch and real-time data processing pipelines for large datasets.

Lead data lake, lakehouse, and data warehouse implementations.

Provide architectural guidance on performance optimization, scalability, governance, security, and reliability.

Work closely with business stakeholders to understand analytical and reporting requirements.

Review technical designs, conduct architecture assessments, and establish best practices.

Mentor data engineers and technical leads across project engagements.

Lead technology evaluations, proof-of-concepts (POCs), and solution accelerators.

Drive cloud migration and modernization initiatives from legacy data platforms.

Ensure adherence to enterprise security, compliance, and data governance standards.

Collaborate with DevOps and platform teams to implement CI/CD and infrastructure automation for data solutions.

Additional Responsibilities

Enterprise Data Architecture

Solution Design & Governance

Technology Strategy & Roadmap

Presales & Client Advisory

Architecture Reviews

Data Platform Modernization

Technical Leadership & Mentoring

Cost Optimization & Non-Functional Requirements

Stakeholder Engagement

Technical and Professional Requirements

11+ years of experience in Data Engineering, Analytics, or Data Platform Architecture.

Strong expertise in Databricks Lakehouse Platform.

Extensive hands-on experience with Snowflake Data Cloud.

Expert-level proficiency in PySpark and distributed data processing.

Strong experience in designing enterprise-scale data lakes, data warehouses, and lakehouse architectures.

Hands-on experience with cloud platforms such as:

Azure (ADF, ADLS Gen2, Synapse, Azure Databricks)

AWS (S3, Glue, EMR, Redshift)

GCP (BigQuery, DataProc, Cloud Storage)

Strong SQL and data modeling skills (Dimensional Modeling, Data Vault, Star Schema).

Experience building ETL/ELT frameworks and reusable data engineering accelerators.

Knowledge of Data Governance, Metadata Management, Data Lineage, and Data Quality frameworks.

Experience with orchestration tools such as Airflow, Azure Data Factory, or similar.

Expertise in performance tuning and cost optimization within Databricks and Snowflake environments.

Experience implementing CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or similar tools.

Preferred Skills

• Databricks

Educational Requirements

Bachelor of Engineering