Data Engineer (Azure, Fabric, Databricks)

Collectiv • Chicago, IL

4d • Remote

About The Position

The Data Engineer is responsible for designing, implementing, and supporting modern data platforms using Microsoft Fabric and Databricks , with a strong emphasis on lakehouse architecture, analytics engineering, and semantic modeling. This role is client-facing and requires translating business requirements into scalable technical solutions while adhering to data engineering best practices. You will collaborate with client stakeholders, Collectiv consultants, and cross-functional teams to deliver data platforms that support reporting, advanced analytics, and self-service BI.

Requirements

• Bachelor's degree from an accredited university
• 4+ years of experience in data engineering, analytics engineering, or data platform consulting
• Hands-on experience with Microsoft Fabric and/or Databricks in production environments (experience with both strongly preferred)
• Strong SQL skills, including performance tuning and optimization
• Experience designing data models for analytics (star schemas, semantic models, lakehouse patterns)
• Experience working in cloud data platforms (Azure preferred)
• Ability to work independently in a client-facing consulting role

Nice To Haves

• Experience migrating or modernizing data platforms (e.g., legacy DW to lakehouse)
• Familiarity with Power BI dataset modeling and performance optimization
• Experience integrating data platforms with downstream applications or APIs
• Consulting experience with multiple clients or industries
• Strong written and verbal communication skills
• Relevant Azure certifications (e.g., Azure Solutions Architect, Azure Data Engineer), Fabric (DP-600, DP-700) certifications, and Databricks certifications are highly desirable

Responsibilities

• Design and implement modern data platforms leveraging Microsoft Fabric (OneLake, Lakehouse, Data Factory, Power BI) and Databricks (Spark, Delta Lake, SQL Warehouses)
• Build and optimize data ingestion, transformation, and orchestration pipelines using ELT/ETL best practices
• Develop scalable lakehouse architectures that support analytics, reporting, and downstream data products
• Translate business and analytical requirements into technical designs and data models
• Develop and maintain semantic models that enable performant reporting and self-service analytics
• Partner with analytics and business teams to deliver trusted datasets and dashboards (Power BI or equivalent)
• Ensure data quality, reliability, and usability across the analytics platform
• Write, optimize, and tune SQL and Spark-based transformations for large-scale analytical workloads
• Apply version control, CI/CD concepts, and code review standards to data engineering workflows
• Ensure solutions meet quality criteria related to performance, scalability, and maintainability
• Work directly with client stakeholders to gather requirements, communicate technical concepts, and manage expectations
• Collaborate with project managers, architects, and fellow consultants to align technical delivery with project goals
• Contribute to internal documentation, reusable patterns, and knowledge sharing across Collectiv

Benefits

• Join Collectiv for a rewarding career in a fully remote work environment with quarterly in person team experiences and a competitive compensation.
• We provide a clear growth trajectory with professional development opportunities and a top-shelf benefits package.
• Enjoy unlimited time off and 100% covered health insurance, encompassing medical, dental, and vision plans for you and your family.
• Our comprehensive offerings, from a competitive 401(k) with a 4% match to educational assistance, tech stipend, and discounts on various goods and services, demonstrate our commitment to your well-being and success.
• At Collectiv, your career thrives with a perfect blend of remote flexibility, growth potential, and unparalleled benefits prioritizing your financial, physical, and emotional well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level