Remote Data & Analytics Engineer: Lakehouse + Power BI

Collectiv; Oregon; Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

Fully remote work environment

Quarterly in-person team experiences

Unlimited time off

Health insurance (100% covered)

Job summary

Collectiv is seeking a Data & Analytics Engineer to bridge data platforms and analytics delivery. You will design lakehouses on Microsoft Fabric and Databricks, build robust ELT/ETL pipelines, and deliver complex Power BI reports for enterprise clients.

You will translate ambiguous business needs into scalable data models, optimize SQL/Spark workloads, and collaborate with stakeholders to ensure trusted datasets and analytics-ready dashboards.

Qualifications

• Bachelor's degree required or equivalent experience.
• 5+ years in data/analytics engineering or BI consulting.
• Hands-on with Microsoft Fabric and/or Databricks.
• Advanced Power BI with data modeling and DAX.
• Strong SQL with optimization and analytics.
• Experience designing analytics data models (star schemas, semantic models).
• Client-facing consulting experience and proactive communication.

Responsibilities

• Design and implement modern data platforms using Microsoft Fabric and Databricks.
• Build and optimize ELT/ETL pipelines and lakehouse architectures.
• Develop enterprise-scale Power BI reports and semantic models.
• Translate requirements into scalable data models and transformations.
• Write and optimize SQL and Spark transformations for large workloads.
• Collaborate with clients and internal teams, ensuring quality and delivery.

Skills

Power BI

SQL

Spark

DAX

Data modeling

Client-facing

CI/CD

Education

Bachelor's degree

Tools

Microsoft Fabric

Databricks

Delta Lake

Job description

Collectiv is seeking a Data & Analytics Engineer to bridge data platforms and analytics delivery. You will design lakehouses on Microsoft Fabric and Databricks, build robust ELT/ETL pipelines, and deliver complex Power BI reports for enterprise clients.

You will translate ambiguous business needs into scalable data models, optimize SQL/Spark workloads, and collaborate with stakeholders to ensure trusted datasets and analytics-ready dashboards.