About the Role

The BI & Semantic Model Engineer serves as the critical bridge between raw data lakehouses/warehouses and business intelligence consumers. In this role, you design, build, and optimize governed, enterprise-grade semantic layers that translate complex distributed data schemas into intuitive, performant business models. Rather than simply building front-end charts, you focus on data modeling rigor, calculation design, query performance optimization, and self-service enablement. You will establish single-source-of-truth metric definitions, implement granular security policies (RLS/OLS), and optimize high-throughput analytics pipelines across enterprise BI platforms (such as Microsoft Fabric, Power BI, Cube, or dbt Semantic Layer) and modern cloud data warehouses.

Responsibilities

- Enterprise Semantic Modeling: Architect, build, and maintain centralized tabular semantic models, dimensional models (star/snowflake schemas), and universal metrics layers that serve disparate business units and analytical workloads.
- Calculation & Metric Standardization: Develop, test, and document complex business logic, KPIs, time-intelligence calculations, and metric definitions using advanced analytical languages (DAX, MDX, or semantic engine code like Cube.js / MetricFlow).
- Performance Tuning & Storage Engine Optimization: Optimize query response times, compression, memory footprints, and partition schemes. Transition high-volume datasets across optimal storage modes (Direct Lake, In-Memory/Import, DirectQuery, Aggregations, and Composite models).
- Tooling & Code-First Lifecycle Management: Maintain semantic models as code using developer tooling (Tabular Editor, ALM Toolkit, dbt, or BIML). Manage schema migrations, metadata versioning, and CI/CD deployment pipelines using Git and Azure DevOps/GitHub.




- Security & Governance Architecture: Design and enforce data governance protocols, including dynamic Row-Level Security (RLS), Object-Level Security (OLS), data masking, and sensitivity labels across shared datasets.
- Data Integration & Transformation: Collaborate with Data Engineers to shape raw lakehouse/warehouse schemas (Snowflake, Databricks, Fabric Lakehouse, BigQuery) into analytics-ready curated views and facts/dimensions.
- Auditing & Query Diagnostics: Profile slow-running reports, identify inefficient query execution plans using DAX Studio or database query profilers, and tune relationship cardinalities, filter context evaluation, and materialized aggregates.

Requirements

- Experience: 3–6 years of dedicated experience in enterprise BI engineering, data modeling, or analytics engineering with a primary focus on semantic layer architecture.Advanced Semantic Modeling & DAX: Deep technical mastery of tabular modeling engines, star schemas, bidirectional filtering nuances, and writing productive, highly performant DAX calculations and window functions.Data Warehousing & SQL Mastery: Exceptional SQL skills for complex data querying, transformation, window functions, and designing optimal dimensional schemas ($Kimball$ methodology).Cloud Data Platforms: Production experience sourcing data from cloud data warehouses/lakehouses such as Snowflake, Azure Synapse / Fabric, Databricks (Delta Lake), or Google BigQuery.Model Engineering Tooling:



Hands-on experience with modern semantic developer tools like Tabular Editor 2/3, DAX Studio, and Git-integrated BI deployment workflows.Storage Modes & Data Refresh Architecture: Working knowledge of optimizing incremental refreshes, hybrid partitions, DirectQuery caching limits, and modern engine features like Direct Lake on Delta Parquet files.Data Governance & Security: Proven track record implementing scalable dynamic security frameworks (RLS/OLS) driven by Active Directory/Entra ID groups or custom entitlement tables.

Nice to Have

- Universal Semantic Layers: Experience with headless or universal semantic layer tools (e.g., Cube, dbt Semantic Layer / MetricFlow, GoodData, or AtScale).
- Scripting & Automation: Proficiency in Python or PowerShell for programmatic model management (e.g., using Tabular Object Model / TOM, PyPowerBI, or Semantic Link / SemPy).
- XMLA / API Operations: Familiarity with script-based dataset management via XMLA endpoints, TMSL (Tabular Model Scripting Language), or Fabric/Power BI REST APIs.
- Modern Orchestration: Familiarity with data transform orchestration tools such as dbt Core/Cloud, Apache Airflow, or Fabric Data Factory.
- Certifications: Microsoft Certified: Fabric Analytics Engineer Associate (DP-600), Microsoft Certified: Power BI Data Analyst Associate (PL-300), or SnowPro Core Certification.

Pay: ₹100,000.00 - ₹130,000.00 per month

Benefits:

- Health insurance
- Life insurance
- Provident Fund

Application Question(s):

- Do you have experience in Data Security & Governance?
- Do you have Experience in Git/Azure DevOps?

Experience:

- Advanced Power BI Semantic Modeling & DAX: 3 years (Required)

Work Location: Hybrid remote in Coimbatore, Tamil Nadu

📌 BI & Semantic Model Engineer (Coimbatore)
🏢 GUHATEK CONSULTING SERVICES PRIVATE
📍 Coimbatore