Data Engineer

Drawing inspiration from the movie The Matrix, QuontX envisions a groundbreaking technology that transcends AI, deeply exploring and understanding the multifaceted complexity of society. We build a true-to-life virtual replica of reality, empowering organizations to run precise financial risk analyses and what-if explorations at global scale.

We're seeking a Neo, someone who can free humanity from the haze of uncertainty and shatter the seemingly impenetrable barrier of complexity. In collaboration with academic experts in complex systems dynamics, data quality & advanced machine learning, you will make the uncertain future less uncertain.

• Data Architecture & Pipeline Development: Design, build, and optimize scalable, high-performance data pipelines to ingest, process, and integrate diverse, large-scale datasets from societal and real-world sources, ensuring robust data flow for the virtual replica of reality.
• Data Infrastructure Optimization: Develop and maintain efficient, reliable data infrastructure to support high-fidelity data processing and storage, enabling seamless access and performance for advanced financial risk & scenario analysis.
• Data Integration & Collaboration: Partner with interdisciplinary teams to ensure data systems are interoperable and aligned with analytical needs, implementing innovative solutions to enhance data quality and accessibility for risk modeling efforts.

• 5+ years of experience in data engineering or a closely related field, with a focus on designing and implementing large-scale, high-performance data systems.
• Proven expertise in building and optimizing end-to-end data pipelines, from ingestion and processing to integration of diverse, complex datasets for real-world applications.
• Strong proficiency in developing scalable data architectures, including expertise in cloud platforms, distributed systems, and database technologies to ensure robust and efficient data infrastructure.
• Experience collaborating with interdisciplinary teams, ideally bridging technical data engineering with domain-specific requirements, such as those in social sciences or complex systems, to ensure high-quality data integration.