{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/data-pipeline-engineering"},"x-facet":{"type":"skill","slug":"data-pipeline-engineering","display":"Data Pipeline Engineering","count":2},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_9ca997fb-218"},"title":"Quantitative Developer","description":"<p>We are building a world-class systematic data platform that will power the next generation of our systematic portfolio engines.</p>\n<p>The systematic data group is looking for a Quantitative Developer to join our growing team. The team consists of content specialists, data scientists, engineers, and quant developers who are responsible for discovering, maintaining, and analysing sources of alpha for our portfolio managers.</p>\n<p>The role builds on individual&#39;s knowledge and skills in four key areas of quantitative investing: data, statistics, technology, and financial markets.</p>\n<p>Principal Responsibilities:</p>\n<ul>\n<li>Use finance knowledge and statistical knowledge to analyse potential alpha sources and present findings to portfolio managers and quantitative analysts.</li>\n<li>Build quant tools to help portfolio managers research, evaluate, combine alphas, and understand risks.</li>\n<li>Design and maintain tools to evaluate and monitor data quality and integrity for a wide variety of data sources.</li>\n<li>Engage with vendors, brokers, and perform analytics to understand characteristics of datasets.</li>\n<li>Interact with portfolio managers and quantitative analysts to understand their use cases and recommend datasets to help maximise their profitability.</li>\n</ul>\n<p>Skills Required:</p>\n<ul>\n<li>3+ years of work experience as a financial engineer, data scientist, or quant developer.</li>\n<li>Strong knowledge of Python and/or C++, Java, C#.</li>\n<li>Familiarity with data pipeline engineering, ETL for large datasets, and scheduling tools like Airflow.</li>\n<li>Strong SQL and database experience including PL-SQL or T-SQL.</li>\n<li>Understanding of typical software development lifecycle and familiarity with: Linux, GitHub, CI/CD.</li>\n<li>Ph.D. or Masters in computer science, mathematics, statistics, or other field requiring quantitative analysis.</li>\n</ul>\n<p>Beneficial Skills and Experience:</p>\n<ul>\n<li>Understanding of risk models and performance attribution.</li>\n<li>Experience with financial markets such as equities and futures.</li>\n<li>Knowledge of statistical techniques and their usage.</li>\n</ul>\n<p>The estimated base salary range for this position is $165,000 to $250,000, which is specific to New York and may change in the future. Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_9ca997fb-218","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Equity IT","sameAs":"https://mlp.eightfold.ai","logo":"https://logos.yubhub.co/mlp.eightfold.ai.png"},"x-apply-url":"https://mlp.eightfold.ai/careers/job/755952876477","x-work-arrangement":"onsite","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$165,000 to $250,000","x-skills-required":["Python","C++","Java","C#","data pipeline engineering","ETL","Airflow","SQL","database","Linux","GitHub","CI/CD","Ph.D.","Masters"],"x-skills-preferred":[],"datePosted":"2026-04-18T22:12:44.538Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York, New York, United States of America"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Finance","skills":"Python, C++, Java, C#, data pipeline engineering, ETL, Airflow, SQL, database, Linux, GitHub, CI/CD, Ph.D., Masters","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":165000,"maxValue":250000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_baad2598-8bc"},"title":"Staff / Senior Software Engineer, Compute Capacity","description":"<p><strong>About the Role</strong></p>\n<p>Anthropic&#39;s Accelerator Capacity Engineering (ACE) team manages one of the largest and fastest-growing accelerator fleets in the industry. As an engineer on ACE, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on.</p>\n<p><strong>What This Team Owns</strong></p>\n<p>The team’s work spans three functional areas: data infrastructure, fleet observability, and compute efficiency. Depending on your background and interests, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap:</p>\n<p><strong>Data Infrastructure</strong></p>\n<p>Collecting, normalizing, and serving the fleet-wide data that powers everything else. This means building pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalizing billing and usage data across cloud providers, and maintaining the BigQuery layer that the rest of the org queries against.</p>\n<p><strong>Fleet Observability</strong></p>\n<p>Making the state of the accelerator fleet legible and actionable in real time. This means building cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and driving systemic improvements to scheduling and fragmentation.</p>\n<p><strong>Compute Efficiency</strong></p>\n<p>Measuring and improving how effectively every major workload uses the hardware it’s running on. This means instrumenting utilization metrics across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and collaborating directly with system-owning teams to close efficiency gaps.</p>\n<p><strong>What You’ll Do</strong></p>\n<ul>\n<li>Build and operate data pipelines that ingest accelerator occupancy, utilization, and cost data from multiple cloud providers into BigQuery.</li>\n<li>Develop and maintain observability infrastructure , Prometheus recording rules, Grafana dashboards, and alerting systems , that surface actionable signals about fleet health, occupancy, and efficiency.</li>\n<li>Instrument and analyze compute efficiency metrics across training, inference, and eval workloads.</li>\n<li>Build internal tooling and platforms that enable capacity planning, workload attribution, and cluster debugging.</li>\n<li>Operate Kubernetes-native systems at scale , deploying data collection agents, managing workload labeling infrastructure, and understanding how taints, reservations, and scheduling affect capacity.</li>\n<li>Normalize and reconcile data across heterogeneous sources , including AWS, GCP, and Azure billing exports, vendor-specific telemetry formats, and internal systems with different schemas and billing arrangements.</li>\n</ul>\n<p><strong>You May Be a Good Fit If You Have</strong></p>\n<ul>\n<li>5+ years of software engineering experience with a strong track record building and operating production systems.</li>\n<li>Kubernetes fluency at operational depth , you’ve operated production K8s at meaningful scale, not just written manifests.</li>\n<li>Data pipeline engineering experience , designing, building, and owning the full lifecycle of production data pipelines.</li>\n<li>Observability tooling experience , Prometheus, PromQL, and Grafana are in the critical path for this team.</li>\n<li>Python and SQL at production quality.</li>\n<li>Familiarity with at least one major cloud provider (AWS, GCP, or Azure) at the infrastructure level , compute, billing, usage APIs, cost management tooling.</li>\n</ul>\n<p><strong>Strong Candidates May Also Have</strong></p>\n<ul>\n<li>Multi-cloud data ingestion experience , especially working with AWS and GCP APIs, billing exports, or vendor-specific telemetry formats.</li>\n<li>Accelerator infrastructure familiarity , GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience working with ML training/inference systems at the hardware level.</li>\n<li>Performance engineering and benchmarking experience , building benchmark harnesses, establishing baselines, reasoning about compute efficiency (FLOPs utilization, memory bandwidth, interconnect throughput), and working with system teams to diagnose and improve performance.</li>\n<li>Data-as-product thinking , experience building internal data products with self-service access, schema contracts, API serving, documentation,</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_baad2598-8bc","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.co/","logo":"https://logos.yubhub.co/anthropic.co.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5126702008","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Kubernetes","Python","SQL","Prometheus","Grafana","BigQuery","Cloud computing","Data pipeline engineering","Observability tooling"],"x-skills-preferred":["Multi-cloud data ingestion","Accelerator infrastructure","Performance engineering","Data-as-product thinking"],"datePosted":"2026-04-18T15:56:02.706Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA | New York City, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Kubernetes, Python, SQL, Prometheus, Grafana, BigQuery, Cloud computing, Data pipeline engineering, Observability tooling, Multi-cloud data ingestion, Accelerator infrastructure, Performance engineering, Data-as-product thinking"}]}