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Qualifications
Candidates should possess a strong background in software engineering, with proficiency in programming languages such as Python or Java. Experience in distributed systems, data processing, or machine learning is highly desirable. A solid understanding of data structures and algorithms, as well as the ability to work collaboratively in a fast-paced environment, is essential. Excellent problem-solving skills and a passion for innovation are key attributes we seek.
About the job
About Anyscale:
At Anyscale, we are dedicated to revolutionizing distributed computing, making it accessible to developers of all backgrounds. Our flagship product, Ray, is an influential open-source framework that fosters a thriving ecosystem of libraries for scalable machine learning. Industry giants like OpenAI, Uber, Spotify, Instacart, and Cruise leverage Ray to enhance their AI initiatives, propelling them into practical applications.
With Anyscale, we aim to establish the ultimate environment for Ray, empowering developers and data scientists to effortlessly scale machine learning applications from their local machines to large clusters, without requiring deep expertise in distributed systems.
Ray aspires to deliver a universal API for crafting distributed applications, such as machine learning pipelines that encompass feature engineering, model training, and evaluation. Data is a fundamental component that interconnects these various stages, significantly influencing Ray's usability, performance, and stability. We are seeking talented engineers to enhance, optimize, and scale Ray’s Datasets library and its overall data processing capabilities.
About Anyscale
Anyscale is pioneering the future of distributed computing by making it simpler for developers to build, scale, and manage applications. Our team is at the forefront of the open-source community, dedicated to enhancing the capabilities of Ray and driving transformative change in machine learning. Join us in our mission to enable seamless and efficient distributed systems that empower developers worldwide.
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* Design, implement, and maintain robust data ingestion pipelines that are secure and scalable, sourcing data from diverse systems such as SAP, Salesforce, SharePoint, APIs, and legacy manufacturing platforms.* Develop and enhance metadata-driven services to facilitate discoverability, access governance, and operational transparency of enterprise data.* Act as a technical authority and cross-functional facilitator for the acquisition, quality, and compliance of both structured and unstructured data.* Establish and oversee comprehensive data quality management, including monitoring and reporting.* Contribute to a global data engineering team that supports all major business domains.* Spearhead the ingestion and metadata service implementation for over 100 enterprise data sources.* Collaborate with IT, cybersecurity, infrastructure, and architecture teams to ensure secure and sustainable data delivery.Main Responsibilities:▪ Construct and maintain extraction services using Python or Scala (e.g., Debezium Server, custom APIs, rclone).• Implement Change Data Capture (CDC), delta, and event-based patterns.• Facilitate push-based HTTP and Kerberos-authenticated DLT delivery.• Establish, operate, and troubleshoot SAP extraction using tools like Theobald Extract Universal.• Integrate with systems such as Salesforce, SharePoint, and other API or file-based endpoints.▪ Develop a user-friendly, web-accessible data catalog application, featuring dataset profiles, metadata, and usability enhancements.▪ Integrate dataset discoverability, preview/exploration features, and lineage information using Unity Catalog as a backend metadata system.▪ Design and implement structured access request workflows encompassing submission, approval chains, audit trails, and enablement triggers.• Conduct design reviews with the Cybersecurity team.• Ensure proper documentation and compliance for all interfaces and data ingress points.• Manage audit and traceability requirements.• Collaborate with IT and business users to translate requirements into scalable technical solutions.• Serve as a technical escalation point for complex source integration challenges.▪ Define and execute a multi-layered data quality framework, incorporating unit-level, integration-level, and cross-pipeline validation rules.▪ Establish centralized, version-controlled storage of Data Quality (DQ) rules, integrating them into orchestration and CI/CD pipelines.▪ Implement automated DQ monitoring with varying severity levels (Critical, High, Medium, Low) and enable flagging, filtering, and quarantining mechanisms at relevant pipeline stages.▪ Work closely with source system owners and business stakeholders to define meaningful and actionable DQ thresholds.
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About AQR Capital Management AQR Capital Management is a globally recognized investment management firm that operates at the confluence of financial theory and practical application. Our mission is to achieve superior, sustainable outcomes for our clients by effectively filtering market noise to discern what is truly important, coupled with developing ideas that withstand stringent testing. Our commitment to technological excellence fuels our insights and analysis, establishing us as leaders in both alternative and traditional investment strategies since 1998. We adopt a systematic, research-oriented methodology, leveraging quantitative tools to process fundamental information and manage risk. Our clientele comprises institutional investors, including pension funds, insurance companies, endowments, foundations, and sovereign wealth funds, in addition to financial advisors. The Team: AQR is assembling a new Data Engineering team within the Enterprise Engineering organization to tackle challenges associated with big data. This team will collaborate closely with researchers and other departments throughout the firm to develop and oversee the infrastructure that loads, validates, and provisions extensive datasets utilized in research and investment processes. Your Role: As a Data Engineer, you will engage in the design and construction of a new enterprise data platform employed by quantitative researchers to manage substantial volumes of market, reference, and alternative data, both structured and unstructured. You will also be responsible for developing essential tools that will be utilized firm-wide for data validation, back-testing, and uncovering valuable investment insights. Close collaboration with Researchers and Portfolio Managers will be essential to define and implement detailed business requirements for both new and existing projects. This vital team will leverage big data technologies and cloud-based platforms to create a scalable and extensible solution to meet the data needs of one of the world's largest quantitative investment firms. What You’ll Bring: 6+ years of relevant experience Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field Exceptional coding, debugging, and analytical skills Adept at collaborating with business stakeholders to define and implement solutions Experience working within a distributed data environment and managing large datasets Proficiency in big data frameworks such as Spark and Hadoop
Jan 13, 2026
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