Data Science
Amazon Web Services (AWS)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.
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