Engineering Division DXR Management Vice President Bengaluru

About the role:

We are seeking a Data Engineer to design and implement low-latency data pipelines for transferring data across regions with unique networking challenges. The role involves leveraging cloud services integrating on-premises infrastructure and ensuring robust data governance through strong auditing control frameworks and data filtering. Experience with data cataloging and CBDT-compliant data transfer mechanisms is essential. You will work with both batch and streaming pipelines focusing on low latency and high data quality.

Key Responsibilities

• Hybrid Quantum-Classical Architecture:Architect scalable low-latency backend systems that integrate GSs internal Big Data environments with quantum processors and simulators (e.g. AWS Braket Azure Quantum).
• Data Modeling with Legend:Utilize and extendLegend(GSs open-source data modeling platform) to create logical and physical data models that support high-dimensional financial datasets for quantum state preparation.
• Quantum Data Pipeline Engineering:Design robust pipelines to handle the efficient loading of classical data into quantum-ready formats specifically focusing onblock-encodingsandQuantum Random Access Memory (QRAM)architectures to minimize circuit depth.
• Platform Scalability:Develop the Quantum-as-a-Service internal platform focusing on API design microservices and cloud-native integration (AWS/Private Cloud) to allow GS Quants to execute quantum algorithms seamlessly.
• Algorithm Optimization:Collaborate with Quantum Researchers to optimize data retrieval and storage reducing the data loading bottleneck that currently limits quantum speedup in financial modeling.
• Governance & Security:Ensure all quantum-related data processes comply with GSs rigorous security standards and global financial regulations implementing enterprise-grade data mesh and zero-ETL patterns.

Required Skills & Qualifications

• Backend Expertise:Proficiency inJavaPython orCwith a focus on building distributed systems and high-performance microservices.
• Data Engineering:Extensive experience with big data technologies (e.g.SparkKafkaHadoop) and database design (SQL NoSQL and Graph databases).
• Cloud Infrastructure:Strong knowledge of cloud-native architecture specificallyAWS(Lambda S3 EMR) and containerization (DockerKubernetes).
• Architectural Patterns:Deep understanding of distributed system design data mesh and theLegendmodeling language.
• Quantum Awareness:Conceptual understanding of quantum computing (qubits gates circuit depth) and the specific challenges of mapping classical data to quantum states (e.g. state preparation).
• Education:Bachelors or masters degree in computer science Engineering or a related quantitative field.

Preferred Qualifications

• Experience with financial modeling specificallyMonte Carlo simulationsorBlack-Scholesmodels.
• Familiarity with quantum SDKs such asQiskitBraket orCirq.