01
TRCD-26-06526
Power BI Semantic Modeling & Azure Databricks - Engineer
This contract role will build and optimize an Azure Databricks lakehouse and the Power BI semantic layer that supports enterprise and self-service analytics. The core requirements are hands-on PySpark, Databricks SQL, Unity Catalog and Delta Lake, strong SQL and Kimball dimensional modeling, and Power BI semantic modeling with DAX and storage-mode knowledge. Experience with Tabular Editor, ADLS Gen2, Azure Data Factory, and Entra ID authentication is also specified. The description lists 4+ years of relevant experience, while the structured input gives a 4–8-year band. The role is based in Mumbai; work mode is not specified, and the engagement is contract, with DLT, XMLA, ALM, capacity management, and CI/CD tooling as additional relevant experience.
Position summary
- Location
- India
- Workplace
- On-site
- Employment
- Contract
- Experience
- Minimum 4 years and Maximum 8 years
Role overview
Why This Role Matters.
This contract role will build and optimize an Azure Databricks lakehouse and the Power BI semantic layer that supports enterprise and self-service analytics. The core requirements are hands-on PySpark, Databricks SQL, Unity Catalog and Delta Lake, strong SQL and Kimball dimensional modeling, and Power BI semantic modeling with DAX and storage-mode knowledge. Experience with Tabular Editor, ADLS Gen2, Azure Data Factory, and Entra ID authentication is also specified. The description lists 4+ years of relevant experience, while the structured input gives a 4–8-year band. The role is based in Mumbai; work mode is not specified, and the engagement is contract, with DLT, XMLA, ALM, capacity management, and CI/CD tooling as additional relevant experience.
Your Impact
Deliver Enterprise Value
Help organisations solve complex business problems through modern technology, consulting expertise and measurable outcomes.
Collaboration
Work Across Teams
Collaborate with consultants, architects, engineers and client stakeholders throughout the project lifecycle.
Growth
Learn Continuously
Gain exposure to enterprise technologies, certifications, mentoring and real-world project experience.
Career Path
Grow With Ubique
Build a long-term consulting career with opportunities to take on greater responsibility and leadership over time.
Responsibilities
What You'll Be Doing.
Every role at Ubique contributes directly to solving meaningful business challenges for our clients.
02
Design, build, and maintain production-grade Star Schema and Snowflake data models within Azure Databricks (Unity Catalog).
03
Optimize Delta Lake tables using OPTIMIZE, Z-ORDERING, liquid clustering, partition strategies, and dynamic file sizing for high-speed downstream analytics.
04
Power BI Semantic Layer Engineering
05
Build, publish, and govern enterprise-scale Power BI Semantic Models (formerly Datasets) leveraging Direct Lake, Import, and DirectQuery storage modes.
06
Write clean, optimized DAX measures and enforce star-schema relationships while keeping complex logic upstream in Databricks.
07
Manage XMLA endpoints, deployment pipelines, ALM Toolkit, and Power BI Premium / Fabric capacity utilization.
08
Pipeline & Compute Engineering
09
Build robust ETL/ELT pipelines using PySpark, Databricks SQL, Delta Live Tables (DLT), and Azure Data Factory (ADF).
10
Configure and fine-tune Serverless and Pro Databricks SQL Warehouses to handle concurrent reporting workloads cost-effectively.
11
Governance, Security & Operations
12
Implement Row-Level Security (RLS) and Column-Level Security (CLS) across Unity Catalog and Power BI semantic models.
Technology stack
Tools & Technologies.
The platforms and technologies you'll use to build modern, enterprise-grade solutions.
01
Power BI
Required
02
Power BI Semantic Model
Required
03
DAX
Required
04
Direct Lake
Required
05
DirectQuery
Required
06
Import mode
Required
07
Tabular Editor
Required
08
Azure Databricks
Required
09
Databricks SQL
Required
10
PySpark
Required
11
Unity Catalog
Required
12
Delta Lake
Required
13
Z-Ordering
Required
14
Liquid Clustering
Required
15
ADLS Gen2
Required
16
Azure Data Factory
Required
17
ADF
Required
18
Entra ID
Required
19
Azure AD
Required
20
SQL
Required
Requirements
Skills & Experience.
We value curiosity, collaboration and continuous learning. If you don't meet every requirement but believe you can make an impact, we'd still love to hear from you.
Essential
Required Qualifications
PySpark
Databricks SQL
Unity Catalog
Delta Lake performance tuning, including Z-Ordering and liquid clustering
Power BI semantic model design and storage modes (Direct Lake, DirectQuery, Import)
DAX authoring and performance optimization
Tabular Editor
Azure Data Lake Storage Gen2 (ADLS Gen2)
Azure Data Factory (ADF)
Entra ID authentication
Expert-level SQL
Kimball dimensional modeling, including facts, dimensions, and slowly changing dimensions
Preferred
Nice to Have
Delta Live Tables (DLT)
Databricks SQL Warehouses, including Serverless and Pro
XMLA endpoints
ALM Toolkit and Power BI deployment pipelines
Power BI Premium or Fabric capacity management
CI/CD using Azure DevOps or GitHub Actions
Row-level and column-level security across Unity Catalog and Power BI
What you'll gain
More Than Just A Job.
We're committed to helping every team member grow professionally, personally and technically while working on meaningful projects.
Global Exposure
Collaborate with international clients and multicultural teams on enterprise programmes.
Continuous Learning
Expand your expertise through mentoring, certifications and hands-on project experience.
Career Growth
Take ownership, develop leadership skills and grow your consulting career over time.
Flexible Working
Hybrid and remote collaboration designed around trust and delivering exceptional outcomes.
People First
Join a supportive culture where collaboration, respect and long-term relationships come first.
Enterprise Projects
Work on meaningful technology initiatives for leading organisations across industries.
Apply
Apply for Power BI Semantic Modeling & Azure Databricks - Engineer
One page, about two minutes. We only ask for what we actually need to have a first conversation.