Data Architect (Dremio Lakehouse)

AI Industry

Data Architect (Dremio Lakehouse)

Peak Hire Solutions

Company

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Data Architect (Dremio Lakehouse)

at AI Industry

Agency job

via Peak Hire Solutions

5 - 17 yrs

₹34L - ₹45L / yr

Mumbai, Bengaluru (Bangalore), Hyderabad, Gurugram

Skills

Dremio

Review Criteria:

Strong Dremio / Lakehouse Data Architect profile
5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio
Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems
Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts
Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)
Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics
Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices
Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline
Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies

Preferred:

Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) or data catalogs (Collibra, Alation, Purview); familiarity with Snowflake, Databricks, or BigQuery environments

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.

Review Criteria:

Strong Dremio / Lakehouse Data Architect profile
5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio
Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems
Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts
Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)
Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics
Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices
Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline
Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies

Preferred:

Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) or data catalogs (Collibra, Alation, Purview); familiarity with Snowflake, Databricks, or BigQuery environments

Role & Responsibilities:

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.

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