Role Overview
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.
Key Responsibilities
Client Delivery & Solution Design
- 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
Analytics & BI Enablement
- 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
Engineering Best Practices
- 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
Consulting & Collaboration
- 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
Required Qualifications
- 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
Preferred Qualifications
- 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
What Success Looks Like in This Role
- Clients trust you as a technical advisor for their data platform decisions
- Delivered solutions are scalable, well-documented, and aligned to best practices
- Business users are enabled through reliable data models and analytics-ready datasets
- You actively contribute to Collectiv’s data platform standards and consulting excellence
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.
Collectiv is the leading boutique consulting and strategy firm specializing in Analytics, Planning and AI with Power BI and the Microsoft Data Stack.
Our mission at Collectiv is simple: We help enterprises make better decisions.
Our Core Values:
Growth Mindset – Bring solutions, not problems.
Excel with Humility – Be humbly brilliant.
Communicate Actively and Empathetically – Listen, ask, understand, help.
Work Hard, Play Hard – Results are proportional to effort.
Keep Calm and Carry On – Stay cool, calm and Collectiv.
Skills Required
- Bachelor’s degree from an accredited university
- 4+ years of experience in data engineering, analytics engineering, or data platform consulting
- Production experience with Microsoft Fabric and/or Databricks
- Strong SQL skills, including performance tuning and optimization
- Experience designing analytics data models, including star schemas, semantic models, and lakehouse patterns
- Experience working with cloud data platforms, preferably Azure
- Ability to work independently in a client-facing consulting role
- Experience migrating or modernizing data platforms, such as legacy data warehouses to lakehouses
- 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, Microsoft Fabric, or Databricks certifications
What We Do
Voted the #1 consulting provider on G2 for Business Intelligence and Power BI, Collectiv’s experts are highly skilled across the Microsoft data stack and highly specialized in planning and forecasting.
