Senior Databricks Architect & Data Platform Lead

Tiger Analytics Inc.; United States; Remote; USD 150,000 - 220,000; Full time

Job summary

Tiger Analytics is seeking a Databricks Architect to design and deploy scalable data solutions on the Databricks platform for enterprise clients. The role combines architecture, hands-on development, and governance across AWS services.

You will lead data engineering efforts, optimize Spark pipelines, and guide clients on best practices for secure, cost-efficient data platforms while working in a fast-growing, entrepreneurial environment.

Qualifications

• 12+ years of experience in architecture and data engineering of large-scale data ecosystems.
• Databricks design and architecture focusing on scalable, secure solutions.
• Hands-on Databricks/DataOps architecture to improve performance and monitoring.
• Good exposure to AWS and leadership skills; able to work independently.
• Experience with Hadoop environments and Spark streaming pipelines.

Responsibilities

• Design and implement scalable end-to-end data solutions on Databricks for ETL, streaming, and ML workloads.
• Provide technical leadership on Databricks adoption and reference architectures.
• Tune Spark jobs and optimize data pipelines for performance and cost efficiency.
• Architect, develop, and debug PySpark jobs; integrate Databricks with AWS services.
• Implement data governance, security, and access controls in the Databricks environment.
• Collaborate with clients to translate business problems into data engineering solutions.
• Drive performance improvement initiatives and monitoring tooling.

Skills

Databricks

PySpark

AWS

Data governance

Security

Leadership

Performance tuning

Big data

Tools

Unity Catalog

EMR

Lambda

S3

Hadoop

Job description

Tiger Analytics is seeking a Databricks Architect to design and deploy scalable data solutions on the Databricks platform for enterprise clients. The role combines architecture, hands-on development, and governance across AWS services.

You will lead data engineering efforts, optimize Spark pipelines, and guide clients on best practices for secure, cost-efficient data platforms while working in a fast-growing, entrepreneurial environment.