Senior Data Engineer - Application

About Solidus LabsAt Solidus, we are shaping the financial markets of tomorrow by providing cutting-edge trade surveillance technology that protects investors, enhances transparency, and ensures regulatory compliance across traditional assets, prediction markets, and crypto. With over 20 years of experience in developing Wall Street-grade FinTech, our team delivers innovative solutions that financial institutions and regulators worldwide rely on to detect, investigate, and report market manipulation, financial crime, and fraud. Headquartered on Wall Street, with offices in Singapore, Tel Aviv, and London, we safeguard millions of retail and institutional entities globally, monitoring over a trillion events each day.The RoleWe’re looking for a strong Software Engineer with experience in Data Engineering. Someone who is proficient in building robust, scalable, maintainable and thoroughly monitored data pipelines on cloud environments.As an ambitious start-up in an extremely dynamic space, we pride ourselves on being independent, accountable, and organized, with a self-starter attitude and a willingness to get our hands dirty with day-to-day work that might fall outside our official scope, while keeping an eye on our goals and the big picture.Responsibilities:Design and implement customer-facing integrations of various asset types into the data pipelinesDesign and develop the data team's micro services - Java services running on K8S and in 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 data integrations, FIX, CSV, JSON, and other formats - including market (e.g., order book) and reference (e.g., company information) data. AI-first solution approach. Coding using AI agents, such as Claude, for a given problem context, with adherence to the architectural and coding conventions of the organizationPlan and communicate integrations with other teams that consume the data and use it to create insights. Strong collaboration with product and customer facing teamsTackle data duplication, velocity, schema adherence (and schema versioning), high availability, data governance, and more.