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Solidus Labs is a fintech company providing trade surveillance technology for financial markets, protecting investors and ensuring regulatory compliance across traditional assets, prediction markets, and crypto. The company has 20+ years of Wall Street-grade fintech experience and operates globally from headquarters on Wall Street with offices in Singapore, Tel Aviv, and London. They monitor over a trillion events daily for financial institutions and regulators worldwide.
You will join the data engineering team as a Senior Data Engineer responsible for designing and implementing robust, scalable data pipelines on cloud environments. Your responsibilities include:
- Design and implement customer-facing integrations of various asset types into the data pipelines
- Design and develop the data team's microservices—Java services running on Kubernetes and AWS
- Develop, design, and maintain end-to-end ETL workflows, including data ingestion and transformation logic across multiple data sources
- Enrich financial data through third-party integrations, handling FIX, CSV, JSON, and other formats for both market data (e.g., order books) and reference data (e.g., company information)
- Apply AI-first solution approaches, using AI agents such as Claude for problem-solving while adhering to organizational architectural and coding conventions
- Plan and communicate integrations with other teams consuming the data; collaborate closely with product and customer-facing teams
- Address data challenges including duplication, velocity, schema adherence and versioning, high availability, and data governance
The company values independence, accountability, organization, self-starter attitudes, and willingness to tackle work beyond official scope while maintaining focus on goals and the bigger picture.
Requirements: The posting indicates need for strong software engineering experience with data engineering focus, proficiency in building robust and scalable data pipelines on cloud environments, and experience with Java services, Kubernetes, AWS, ETL workflows, and multiple data formats (FIX, CSV, JSON). Experience with financial data and market surveillance is implied by the domain context.
