2+ Dimensional modeling Jobs in Bangalore (Bengaluru) | Dimensional modeling Job openings in Bangalore (Bengaluru)

We are looking for an experienced Data Modeler with strong expertise in Conceptual, Logical, and Physical Data Modeling across enterprise-scale data platforms. The ideal candidate will have hands-on experience designing data solutions for Data Warehouses, Data Lakes, Lakehouses, Data Marts, OLTP, and OLAP systems, along with deep exposure to dimensional and enterprise data modeling methodologies.

The role requires close collaboration with business stakeholders, architects, engineering teams, and leadership to design scalable, high-performance, and governance-driven data ecosystems.

Key Responsibilities

Data Modeling & Architecture

• Design and maintain Conceptual, Logical, and Physical Data Models for enterprise applications.
• Develop scalable models for:
• Data Warehouses
• Data Lakes & Lakehouses
• Data Marts
• OLTP & OLAP systems
• Create ER diagrams using tools such as:
• ERwin
• ER/Studio
• Enterprise Architect
• PowerDesigner
• Implement:
• Entity-Relationship Modeling
• Data Vault Modeling
• Dimensional Modeling
• Suggest optimal modeling approaches based on business requirements and target architecture.
• Define and enforce enterprise data modeling standards and best practices.

Data Management & Governance

• Perform:
• Data Extraction
• Data Analysis
• Data Cleansing
• Data Mapping
• Data Profiling
• Create and maintain:
• Source-to-Target Mapping documents
• Data Dictionaries
• Functional Specifications
• Champion:
• Data Lineage
• Metadata Management
• Data Quality Analysis
• Data Governance standards
• Define data retention policies and automated anomaly detection mechanisms.

Collaboration & Delivery

• Collaborate with:
• Business Stakeholders
• Data Owners
• Business Analysts
• Architects
• Data Engineers
• QA Teams
• Guide teams on:
• Data ingestion logic
• Ingestion frequency
• Data consumption patterns
• Testing strategies
• Monitor project progress and provide updates to leadership on milestones and blockers.
• Mentor junior team members and provide technical guidance on analytics design patterns.

Platform & Engineering Support

• Build scalable ETL/ELT pipelines for large-volume data movement.
• Work with DBAs and Engineering teams to optimize physical data models.
• Support model-driven development and repository management.
• Troubleshoot production data model and service issues.
• Research and recommend modern data management technologies and engineering practices.

Required Skills & Experience

Experience

• 4+ years of experience in enterprise data modeling and analytics environments.
• Experience working with hybrid data ecosystems involving:
• Relational Databases
• Distributed Data Platforms
• Cloud-based Data Systems

Technical Skills

• Strong understanding of:
• Data Warehousing concepts & architecture
• OLTP and OLAP systems
• Data Governance & Data Quality
• Industry Data Models (e.g., ACORD)
• Experience designing complex dimensional data models.
• Exposure to ETL tools and data ingestion frameworks.
• Hands-on experience with cloud platforms:
• AWS
• Azure
• GCP

Data Modeling Tools

Experience with one or more of the following:

• ERwin
• PowerDesigner
• ER/Studio
• Enterprise Architect
• MagicDraw
• Business Glossary tools

Leadership & Communication

• Experience leading large teams and enterprise projects.
• Strong stakeholder management and client-facing experience.
• Excellent verbal and written communication skills.
• Ability to explain complex technical concepts to non-technical stakeholders.
• Strong analytical, troubleshooting, and problem-solving skills.
• Familiarity with Agile methodologies.

Preferred Qualifications

• Experience with Data Vault architecture.
• Experience in insurance industry data models such as ACORD.
• Exposure to metadata-driven and automation-first data engineering practices.
• Experience building semantic layers for BI and analytics solutions.

ROLES AND 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.