Data Engineer - USL/OLAP- Onshore

Required Skills & Experience

• Experience with “AtScale” or OLAP Cubes
• Strong, hands-on expertise in SQL for analytics and data transformation
• Solid Python experience in data engineering contexts
• Deep experience with cloud-based data platforms (GCP / BigQuery preferred)
• Proven experience with semantic layers, OLAP cubes, or analytical modeling tools
• Expert-level understanding of dimensional modeling and analytics architecture
• Ability to balance technical depth with business-facing analytics needs
• Experience operating in production-grade, scalable data environments

Nice to Have Skills & Experience

• Experience enabling analytics at enterprise scale
• Background working closely with BI, analytics engineering, or data science teams
• Exposure to multiple semantic layer or BI technologies

Job Description

We’re looking for a Staff-level Data Engineer who thrives at the intersection of data engineering, analytics, and business intelligence. This role is ideal for someone who enjoys building scalable data pipelines, high performance analytical models, and semantic layers that enable self service analytics across an organization. You’ll play a key role in designing and owning data foundations that power dashboards, metrics, and decision making at scale - partnering closely with analytics, BI, and business stakeholders.

What You’ll Be Doing Core Data Engineering

• Design, build, and maintain robust ETL/ELT pipelines in modern cloud environments (preferably GCP / BigQuery)
• Write advanced SQL for large-scale analytical workloads, including complex joins, window functions, and performance optimization
• Use Python for data processing, automation, and pipeline development
• Implement and manage orchestration workflows using tools like Airflow / Cloud Composer
• Support CI/CD processes for data pipelines (e.g., Jenkins or similar)
• Ensure data quality, validation, monitoring, and production reliability
• Troubleshoot and support data issues in production environments

Semantic Layer & OLAP Modeling (High Priority)

• Design and build semantic layers / universal semantic models that sit on top of large analytical datasets
• Work hands-on with OLAP / cube technologies such as AtScale, SSAS, or similar multi-dimensional analytics tools

Develop and optimize:

• Measures, metrics, and calculated fields
• Hierarchies and dimensions
• Aggregations and performance strategies
• Tune query performance across large datasets to support fast, interactive analytics
• Enable governed, self-service analytics for BI tools and business users

Data Modeling & Analytics Architecture

• Design dimensional data models aligned to business processes

Build and optimize:

• Star schemas
• Snowflake schemas
• Fact and dimension tables
• Translate complex business logic into scalable, reusable data models
• Create metric layers or reusable business logic abstractions used consistently across analytics platforms