Remote Azure Data Engineer for AI & Analytics

Ladders; United States; On-site; USD 156,000 - 181,000; Full time

Benefits offered by this job

Remote working flexibility

Home office setup support

Continuous learning in Microsoft tech

Job summary

Ladders is seeking a Solutions Engineer - Data Engineering to join its growing technical services team. This remote US-based role focuses on delivering modern data engineering solutions for analytics, AI, and reporting initiatives.

You will collaborate with stakeholders to design secure, scalable data platforms for public sector and regulated environments, ensuring data quality, reliability, and compliance across multi-source datasets.

Qualifications

• 5–7 years of hands-on data engineering experience with Azure and/or Fabric.
• Proficient in designing production data platforms with data modeling.
• Strong knowledge of data security and regulatory compliance.
• Experience delivering projects with both technical and non-technical stakeholders.
• Familiarity with CI/CD and modern engineering practices.

Responsibilities

• Design and implement data pipelines using Azure Data Factory and related tools.
• Build scalable data lakehouse solutions ensuring security and reliability.
• Maintain and optimize ETL/ELT processes for multi-source datasets.
• Support data governance and validation for quality assurance.
• Collaborate with analytics teams to prepare data for AI and reporting needs.

Skills

Data modeling

Stakeholder collaboration

CI/CD practices

Security & compliance

Production data platforms

Tools

Azure Data Factory

Microsoft Fabric

Data Lakehouse

Version control

ETL/ELT pipelines

Job description

Ladders is seeking a Solutions Engineer - Data Engineering to join its growing technical services team. This remote US-based role focuses on delivering modern data engineering solutions for analytics, AI, and reporting initiatives.

You will collaborate with stakeholders to design secure, scalable data platforms for public sector and regulated environments, ensuring data quality, reliability, and compliance across multi-source datasets.