Job Details

Data Engineer

Location: San Francisco, CA or New York, NY

Work Model: Onsite

Compensation: $150,000 – $175,000 base + bonus

About the Company

This organization is a technology-focused investment platform that partners with high-growth and enterprise software businesses to drive long-term value creation. Data, analytics, and technical diligence play a meaningful role in how the firm evaluates opportunities and supports portfolio performance.

The team is investing heavily in modern data capabilities to improve how decisions are made — combining engineering, analytics, and AI to build scalable internal systems that support analytical workflows and operational insights.

The culture is highly collaborative, intellectually curious, and execution-focused. Teams work closely across technical and business functions, valuing ownership, problem-solving, and thoughtful engineering.

About the Role

This is a rare opportunity for an early-career Data Engineer to work at the intersection of data engineering, analytics, and applied AI in a highly analytical environment.

You'll help build the data foundation powering internal AI systems, portfolio analytics, and strategic decision-making tools. This role offers meaningful ownership from day one — combining hands-on engineering with analytical problem-solving across structured data, APIs, cloud infrastructure, and emerging AI workflows.

You will work closely with both technical and commercial stakeholders, helping shape how data is collected, transformed, and operationalized to drive smarter decisions.

Key Responsibilities

• Build and maintain production-grade data pipelines across internal systems, external data sources, APIs, and unstructured datasets
• Design and optimize ETL/ELT workflows that power analytics and AI-enabled applications
• Improve data quality, consistency, and reliability across business-critical datasets
• Partner on data infrastructure, integrations, and architecture decisions to improve scalability and performance
• Develop analytics workflows, dashboards, and reporting solutions that surface actionable insights
• Support engineering improvements to internal analytical systems through automation and monitoring
• Build and maintain API integrations and external data connections
• Document data lineage, schemas, and technical architecture to improve transparency and maintainability
• Provide quantitative and technical support on high-priority analytical initiatives

Must Haves

• 2–4 years of experience in data engineering, analytics engineering, or a similar technical role
• Strong proficiency in SQL and Python for data transformation, analysis, and automation
• Experience building and maintaining production data pipelines
• Hands-on experience with Snowflake or similar cloud data warehouse technologies
• Familiarity with modern data tooling such as dbt, Airflow, or equivalent orchestration/transformation frameworks
• Experience working with cloud infrastructure environments (AWS preferred)
• Strong understanding of ETL/ELT patterns, APIs, and scalable data workflows
• High attention to data quality, reliability, and operational excellence
• Strong communication skills and ability to work across technical and non-technical teams
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related quantitative discipline

Nice to Have

• Experience supporting AI/ML systems or datasets used in model development
• Familiarity with embeddings, retrieval pipelines, or vector-based systems
• Exposure to AI-native development workflows and engineering productivity tools
• Experience building dashboards, monitoring systems, or analytical applications
• Prior experience in financial services or highly analytical business environments

Why Join

• Work on high-impact data and AI initiatives that directly influence strategic decisions
• Strong ownership from day one in a technically challenging environment
• Exposure to a unique mix of engineering, analytics, and applied AI
• Collaborate with highly analytical stakeholders on meaningful business problems
• Clear opportunity for growth, mentorship, and expanded technical responsibility