Data Engineer

Summary

Interval helps enterprises turn messy, underused data into governed, high-confidence intelligence. They are seeking a highly skilled Data Engineer to build and optimize secure, scalable data pipelines and infrastructure that ensure privacy, compliance, and enable AI-powered business transformation.

Responsibilities

• Design, develop, and maintain scalable data pipelines for ingestion, transformation, and delivery of large datasets across diverse industries
• Implement and ensure data privacy and security best practices, supporting data sovereignty and compliance with regulatory requirements
• Collaborate closely with AI/ML engineers, Data Scientists, and Platform engineers to enable advanced analytics and AI capabilities while retaining strict data control
• Optimize data platforms and systems for performance, reliability, and cost efficiency
• Build tools and frameworks for secure, privacy-preserving data processing and orchestration
• Develop and maintain documentation, data models, and technical workflows
• Partner with cross-functional teams to launch new data-driven product features and solutions

Skills

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field
• Proven experience in designing and building ETL pipelines and data infrastructure (cloud, hybrid, and/or on-premise)
• Strong proficiency with Python, SQL, and modern data engineering toolsets (e.g., Apache Spark, Kafka)
• Solid understanding of data security, privacy frameworks, and regulatory compliance such as GDPR, CCPA, or equivalent
• Excellent analytical and communication skills; proactive and detail-oriented
• Experience with privacy-first, AI-native, or data sovereignty-focused platforms is a plus
• Familiarity with industry-specific data challenges (CPG, financial services, energy, supply chain, etc.) is advantageous

Qualifications

Must Haves

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field
• Proven experience in designing and building ETL pipelines and data infrastructure (cloud, hybrid, and/or on-premise)
• Strong proficiency with Python, SQL, and modern data engineering toolsets (e.g., Apache Spark, Kafka)
• Solid understanding of data security, privacy frameworks, and regulatory compliance such as GDPR, CCPA, or equivalent
• Excellent analytical and communication skills; proactive and detail-oriented

Nice to Haves

• Experience with privacy-first, AI-native, or data sovereignty-focused platforms is a plus
• Familiarity with industry-specific data challenges (CPG, financial services, energy, supply chain, etc.) is advantageous

Benefits

• Flexible work
• Meaningful ownership
• Autonomy