Posted Apr 6, 2026
As an experienced Data Architect specializing in Dremio-based data lakehouse environments, your role will involve architecting, implementing, and optimizing data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. You will need to focus on architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments. Key Responsibilities:
Design and implement Dremio lakehouse architecture on cloud platforms such as AWS, Azure, Snowflake, or Databricks ecosystem. - Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads. - Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns. - Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS). - Establish best practices for data security, lineage, and access control aligned with enterprise governance policies. - Support self-service analytics by enabling governed data products and semantic layers. - Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling. - Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data. Qualifications Required:
Bachelor's or Master's degree in Computer Science, Information Systems, or related field. - Minimum of 5 years in data architecture and engineering, with at least 3 years of experience in Dremio or modern lakehouse platforms. - Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena). - Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning. - Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.). - Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC). - Excellent problem-solving, documentation, and stakeholder communication skills. Preferred Skills and Experience:
Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview). - Exposure to Snowflake, Databricks, or BigQuery environments. - Experience in high-tech, manufacturing, or enterprise data modernization programs. Please note that the ideal candidate for this role should possess a strong background in architecture, storage, modeling, enterprise data, cloud technologies, and specifically in Dremio data lakehouse environments. As an experienced Data Architect specializing in Dremio-based data lakehouse environments, your role will involve architecting, implementing, and optimizing data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. You will need to focus on architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments. Key Responsibilities:
Design and implement Dremio lakehouse architecture on cloud platforms such as AWS, Azure, Snowflake, or Databricks ecosystem. - Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads. - Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns. - Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS). - Establish best practices for data security, lineage, and access control aligned with enterprise governance policies. - Support self-service analytics by enabling governed data products and semantic layers. - Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling. - Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data. Qualifications Required:
Bachelor's or Master's degree in Computer Science, Information Systems, or related field. - Minimum of 5 years in data architecture and engineering, with at least 3 years of experience in Dremio or modern lakehouse platforms. - Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena). - Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning. - Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.). - Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC). - Excellent problem-solving, documentation, and stakeholder communication skills. Preferred Skills and Experience:
Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview). - Exposure to Snowflake, Databricks, or BigQuery environments. - Experie
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