Strong Dremio / Lakehouse Data Architect profile :

- Mandatory (Experience 1) - 5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio

- Mandatory (Experience 2) - Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems

- Mandatory (Technical Skills 1) - Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts

- Mandatory (Technical Skills 2) - Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)

- Mandatory (Architecture) - Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics

- Mandatory (Governance) - Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices

- Mandatory (Stakeholder Management) - Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline. 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.