Job Description
Role & Responsibilities
You will be responsible for architecting, implementing, and optimizing Dremio-based data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.
Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/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.
Ideal Candidate
Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
5+ years in data architecture and engineering, with 3+ years 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:
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
You will be responsible for architecting, implementing, and optimizing Dremio-based data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.
Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/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.
Ideal Candidate
Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
5+ years in data architecture and engineering, with 3+ years 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:
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
💡 Quick Summary
Seeking a career-building opportunity? The Data Architect (Dremio Lakehouse) position is now open for candidates interested in the Data Entry Jobs sector. This role in Mumbai offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Data Entry Jobs is a plus.
