Databricks Architect
Centraprise LLC
Boynton Beach, FL, United States
17 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
Tech stack
Unity 3d
Amazon Web Services
Data Analysis
Big Data
Continuous Integration
Data Architecture
Information Engineering
Data Governance
Extract Transform Load (ETL)
Data Transformation
Data Systems
Data Warehousing
+14 more
DevOps
Apache Hive
Meta-Data Management
Query Optimization
Apache Spark
Git
Event Driven Architecture
Data Lakes
Pyspark
Infrastructure Automation Frameworks
Real Time Data
Data Management
Terraform
Databricks
Job description
- Architect and design scalable, secure, and high-performance data platforms using Databricks and AWS.
- Lead solution design, data modeling, and enterprise data architecture initiatives.
- Design and optimize batch and Real Time data pipelines using Databricks, PySpark, and Delta Lake.
- Partner with business and technology stakeholders to define requirements and deliver strategic data solutions.
- Establish standards for data governance, security, quality, performance, and operational excellence.
- Provide technical leadership, mentoring, and architectural guidance to engineering teams.
- Drive cloud modernization, platform optimization, and data transformation initiatives.
Requirements
- 10+ years of experience in Data Engineering and Data Architecture.
- 5+ years of hands-on experience with Databricks, PySpark, and Apache Spark.
- Strong expertise in - Databricks Lakehouse Architecture, Delta Lake, Unity Catalog, Delta Live Tables (DLT), Databricks Workflows, Medallion Architecture
- Experience designing enterprise-scale analytics and data platforms.
- Expert-level proficiency in PySpark and Spark SQL.
- Advanced SQL development and query optimization skills.
- Strong understanding of batch and streaming architectures.
- Experience with data warehousing, ETL/ELT, and large-scale data processing.
- Expertise in dimensional, relational, and analytical data modeling.
- Experience implementing data governance, data quality, and metadata management solutions.
- Knowledge of CI/CD practices, Git, DevOps, and Infrastructure as Code (Terraform preferred).
- Experience establishing architectural standards and engineering best practices.
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