Databricks Data Engineer

Job Description: 

We are seeking a highly experienced Data Modeler to design and implement enterprise-grade data solutions. This role requires deep expertise in data modeling principles, data architecture frameworks, and modern cloud-based platforms. The candidate will play a critical role in shaping our data ecosystem, ensuring scalability, performance, and alignment with business needs.
 
Key Responsibilities-

Data Modeling:

·         Design conceptual, logical, and physical data models for structured and semi-structured data.

·         Define entity relationships, normalization/denormalization strategies, and dimensional modeling for analytics.

·         Develop star and snowflake schemas for data warehouses and analytical workloads.
Ensure data integrity, consistency, and compliance with governance standards.

Data Architecture:

·         Architect end-to-end data solutions leveraging modern frameworks and best practices.

·         Implement Medallion architecture (Bronze, Silver, Gold layers) for scalable data pipelines.

·         Optimize data storage and retrieval strategies for performance and cost efficiency.

·         Collaborate with engineering teams to design data ingestion, transformation, and integration workflows.

Technology Stack:

·         Hands-on experience with Google BigQuery for large-scale analytics.

·         Expertise in Databricks for data engineering and advanced analytics.

·         Familiarity with cloud-native architectures, distributed systems, and data lakehouse concepts.

Required Qualifications:

·         10+ years of experience in data modeling and data architecture.

·         Proven track record in designing enterprise data warehouses and analytical platforms.

·         Strong understanding of Medallion architecture and modern data lakehouse design.

·         Proficiency in SQL, data modeling tools (e.g., ERwin, PowerDesigner), and ETL/ELT frameworks.

·         Hands-on experience with Google BigQuery and Databricks.

Preferred Skills

·         Knowledge of data governance, metadata management, and master data management.

·         Experience with performance tuning and query optimization in large-scale environments.

·         Familiarity with data security and compliance standards.