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Data Architect (Dremio Lakehouse)
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Data Architect (Dremio Lakehouse)
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Data Architect (Dremio Lakehouse)

at AI company

Agency job
5 - 17 yrs
₹30L - ₹45L / yr
Bengaluru (Bangalore), Mumbai, Hyderabad, Gurugram
Skills
Data architecture
Data engineering
SQL
Data modeling
GCS
JDBC
Apache Airflow
Data Transformation Tool (DBT)
Apache Kafka
Spark
Architecture
Data governance
RBAC
skill iconData Science
Tableau
PowerBI
Looker
databricks
skill iconAmazon Web Services (AWS)
Windows Azure
Microsoft Windows Azure
SQL Azure
Analytics
RESTful APIs
Data security
Meta-data management

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

  • Preferred (Nice-to-have) – Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) or data catalogs (Collibra, Alation, Purview); familiarity with Snowflake, Databricks, or BigQuery environments


Job Specific Criteria

  • CV Attachment is mandatory
  • How many years of experience you have with Dremio?
  • Which is your preferred job location (Mumbai / Bengaluru / Hyderabad / Gurgaon)?
  • Are you okay with 3 Days WFO?
  • Virtual Interview requires video to be on, are you okay with it?


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.
Read more
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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