5+ years of experience in Data Engineering, Data Warehousing, or Analytics Engineering roles.
Hands-on experience with Dremio, including semantic layer design and query optimization.
Strong expertise in SQL, data modeling, and dimensional modeling techniques.
Experience with Apache Iceberg for lakehouse implementations.
Solid understanding of Apache Arrow and columnar data processing concepts.
Experience managing data platforms on AWS, including S3, Redshift, and RDS.
Strong knowledge in data virtualization and distributed query processing architectures.
Responsibilities
Design, develop, and maintain semantic data layers using Dremio for self-service analytics.
Build and manage semantic models and curated data products to standardize reporting across teams.
Implement Apache Iceberg-based lakehouse architectures for data management.
Leverage Apache Arrow for high-performance analytical workloads.
Integrate Dremio with AWS data platforms like S3 and Redshift.
Design data virtualization solutions across multiple data sources.
Develop and maintain data models that align with enterprise data standards.
Benefits
Opportunities for professional growth in cutting-edge data technologies.
Access to ongoing training and certification on data platforms.
Flexible working arrangements to promote work-life balance.
Collaborative work culture that values innovation and creativity.
Full Job Description
Responsibilities:
Design, develop, and maintain enterprise-scale semantic data layers using Dremio to support self-service analytics and business intelligence initiatives.
Build and manage semantic models, virtual datasets, business views, and curated data products to ensure consistent reporting and analytics across teams.
Implement and optimize Apache Iceberg-based lakehouse architectures for scalable and reliable data storage and management.
Leverage Apache Arrow technologies to enable high-performance, columnar query processing and analytical workloads.
Integrate Dremio with AWS cloud data platforms including Amazon S3, Amazon Redshift, and Amazon RDS.
Design and implement data virtualization and query federation solutions across multiple data sources.
Develop and maintain logical and physical data models aligned with business requirements and enterprise data standards.
Implement data governance, metadata management, data cataloging, and access control frameworks to ensure data quality and compliance.
Optimize query performance, data access patterns, and workload management across the data platform.
Collaborate with business stakeholders, analytics teams, and data architects to translate reporting and analytics requirements into scalable technical solutions.
Support data platform modernization initiatives and promote best practices for data engineering and analytics enablement.
Required Skills:
5+ years of experience in Data Engineering, Data Warehousing, or Analytics Engineering roles.
Hands-on experience with Dremio, including semantic layer design, virtual datasets, reflections, and query optimization.
Strong expertise in SQL, data modeling, and dimensional modeling techniques.
Experience working with Apache Iceberg for modern lakehouse implementations and table management.
Solid understanding of Apache Arrow and columnar data processing concepts.
Experience integrating and managing data platforms on AWS, including S3, Redshift, and RDS.
Strong knowledge of data virtualization, query federation, and distributed query processing architectures.
Experience implementing data governance, metadata management, data lineage, and security controls.
Familiarity with ETL/ELT processes, data pipelines, and large-scale analytical workloads.
Strong analytical and problem-solving skills with the ability to optimize complex data environments.
Excellent communication skills and ability to work closely with business and technical stakeholders.
