Senior Data Architect
About the role:
We are seeking a seasoned Data Architect with expertise in Databricks (DBX) and AWS-native data services to spearhead the design and implementation of a new data warehouse instance for a major product line. This role will involve building from the ground up—architecting scalable pipelines, optimizing lakehouse performance, and integrating seamlessly with diverse real-time and batch data sources across AWS.
Key Responsibilities:
* Design and deploy a new Databricks Lakehouse instance tailored to the client's product-level data needs.
* Architect and implement robust data ingestion pipelines using Spark (PySpark/Scala) and Delta Lake.
* Integrate AWS-native services (S3, Glue, Athena, Redshift, Lambda) with Databricks for optimized performance and scalability.
* Define data models, optimize query performance, and establish warehouse governance best practices.
* Collaborate cross-functionally with product teams, data scientists, and DevOps to streamline data workflows.
* Maintain CI/CD, preferably DBX for data pipelines using GitOps and Infrastructure-as-Code.
* Monitor data jobs and resolve performance bottlenecks or failures across environments.
Required Skills & Experience:
Databricks / Lakehouse Architecture
* End-to-end setup of Databricks workspaces and Unity Catalog
* Expertise in Delta Lake internals, file compaction, and schema enforcement
AWS Native Integration
* Deep experience with AWS Glue, S3, Redshift Spectrum, Lambda, and Athena
* IAM and VPC configuration knowledge for secure cloud integrations
Data Warehousing & Modeling
* Strong grasp of modern dimensional modeling (star/snowflake schemas)
* Experience setting up lakehouse design patterns for mixed workloads
Automation & DevOps
* Familiarity with CI/CD for data engineering using tools like DBX, Terraform, GitHub Actions, or Azure DevOps
* Proficient in monitoring tools like CloudWatch, Datadog, or New Relic for data pipelines