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Solovis

Manager, Data Engineering

Posted One Month Ago
Remote
Hiring Remotely in United States
Senior level
Remote
Hiring Remotely in United States
Senior level
Lead and grow a data engineering team; define architecture and standards for ETL/ELT pipelines; hands-on design, code review, and development; ensure production reliability, monitoring, and incident response; partner with product and engineering on data roadmap.
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Manager, Data Engineering 

Solovis is a leading portfolio management and analytics platform helping institutional investors navigate today’s complex global markets with clarity and confidence. Backed by Insight Partners, were building the next chapter of growth by investing in people and product to raise the bar on quality and client outcomes. Our team is driven by a culture of disciplined execution, humility, and curiosity where AI is at the core of how we operate, innovate, and serve clients. At Solovis, you'll join a tech-forward, growth-minded team that believes in learning fast, thinking big, and delivering meaningful impact for asset owners worldwide.

This role leads and builds a world-class data engineering team while driving the design, development, and operational excellence of our data platform. This role combines hands-on technical leadership with people management, responsible for hiring top talent, developing engineers, establishing team practices, and ensuring the delivery of scalable, reliable data infrastructure.

Key Responsibilities
  • Define technical standards, architecture, and best practices for data pipelines.

  • Lead design reviews and own technical decisions for ETL/ELT systems.

  • Contribute hands-on: code-review, design, and develop critical data infrastructure.

  • Drive production excellence, monitoring, and incident response.

  • Partner with product and engineering teams on data requirements and roadmap.

Skills
  • Programming: Python, Java

  • Data Engineering: ETL/ELT pipelines, batch processing, data validation, data quality checks, data reconciliation

  • Databases: PostgreSQL, SQL query optimization

  • Cloud: AWS Cloud

  • Containers & Orchestration: Docker, Kubernetes, AKS

  • CI/CD: GitLab pipelines

  • DevOps / Operations: Monitoring, troubleshooting, production support, runbooks

  • AI-assisted development: Claude Code or similar AI coding tool

 
 

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