Job
Description
As an experienced Data Architect specializing in Dremio and modern lakehouse platforms, your role will involve architecting, implementing, and optimizing data lakehouse environments. Your responsibilities will include: - Designing and implementing Dremio lakehouse architecture on cloud platforms such as AWS, Azure, Snowflake, or Databricks ecosystem. - Defining data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads. - Optimizing Dremio reflections, caching, and query performance for diverse data consumption patterns. - Collaborating with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS). - Establishing best practices for data security, lineage, and access control aligned with enterprise governance policies. - Supporting self-service analytics by enabling governed data products and semantic layers. - Developing reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling. - Working closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data. Qualifications required for this role include: - Bachelor's or Master's degree in Computer Science, Information Systems, or a related field. - 5+ years of experience in data architecture and engineering, with at least 3 years working with Dremio or similar modern lakehouse platforms. - Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines such as Presto, Trino, or 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 qualifications include: - Experience integrating Dremio with BI tools such as Tableau, Power BI, Looker, and data catalogs like Collibra, Alation, Purview. - Exposure to Snowflake, Databricks, or BigQuery environments. - Experience in high-tech, manufacturing, or enterprise data modernization programs. This role will allow you to utilize your skills in data, SQL, enterprise architecture, design, data modeling, and performance analysis to drive the success of data lakehouse environments. As an experienced Data Architect specializing in Dremio and modern lakehouse platforms, your role will involve architecting, implementing, and optimizing data lakehouse environments. Your responsibilities will include: - Designing and implementing Dremio lakehouse architecture on cloud platforms such as AWS, Azure, Snowflake, or Databricks ecosystem. - Defining data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads. - Optimizing Dremio reflections, caching, and query performance for diverse data consumption patterns. - Collaborating with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS). - Establishing best practices for data security, lineage, and access control aligned with enterprise governance policies. - Supporting self-service analytics by enabling governed data products and semantic layers. - Developing reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling. - Working closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data. Qualifications required for this role include: - Bachelor's or Master's degree in Computer Science, Information Systems, or a related field. - 5+ years of experience in data architecture and engineering, with at least 3 years working with Dremio or similar modern lakehouse platforms. - Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines such as Presto, Trino, or 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 qualifications include: - Experience integrating Dremio with BI tools such as Tableau, Power BI, Looker, and data catalogs like Collibra, Alation, Purview. - Exposure to Snowflake, Databricks, or BigQuery environments. - Experience in high-tech, manufacturing, or enterprise data modernization programs. This role will allow you to utilize your skills in data, SQL, enterprise architecture, design, data modeling, and performance analysis to drive the success of data lakehouse environments.
