Senior Data Engineer

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<p><strong>OVERVIEW OF 73 STRINGS:</strong></p><p>73 Strings is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. The company's AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation at the click of a button. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit.</p><p>Our 2025 $55M Series B, the largest in the industry, was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, with continued support from Blackstone, Fidelity International Strategic Ventures and Broadhaven Ventures.<br><br><span>About the role</span></p><div><p><span>We are hiring a Senior Data Engineer to build and operate the pipelines, integrations and warehouse that support valuation and monitoring.</span></p></div><div><p><span>You will own production pipelines from source capture through transformation, reconciliation and client delivery, and put them under GitHub, automated test and CI/CD. The platform currently captures change data from relational systems, processes it on Azure Databricks, and delivers it to Snowflake, Microsoft SQL Server and Databricks. The capture method may change. Copied client pipelines are being replaced by metadata-driven components, with data contracts, quality rules and lineage.</span></p></div><p></p><p><span>What you will do</span></p><div><ul><li><p><span>Help redefine the platform’s architecture across ingestion, processing and delivery, so it’s stable, secure and fast enough to support advanced use cases for the business.</span></p></li></ul></div><div><ul><li><p><span>Build and operate batch and streaming pipelines from databases, APIs, event streams and semi-structured sources.</span></p></li></ul></div><div><ul><li><p><span>Implement change data capture and incremental load, including ordering, deletes, replay and slowly changing dimensions.</span></p></li></ul></div><div><ul><li><p><span>Build medallion datasets and dimensional models, and deliver them to Snowflake, Microsoft SQL Server and Databricks.</span></p></li><li><p><span>Apply data contracts, reconciliation and row-level quarantine before publication.</span></p></li></ul></div><div><ul><li><p><span>Own the GitHub workflow and CI/CD, including tests, review, environment promotion and deployment as code.</span></p></li></ul></div><div><ul><li><p><span>Investigate production data failures, and turn requirements from product, valuation and client-facing teams into operable pipelines.</span></p></li></ul><p><span>Requirements</span></p></div><div><ul><li><p><span>10+ years in data engineering on production systems.</span></p></li></ul></div><div><ul><li><p><span>Snowflake or Databricks as a primary platform, including modelling, performance tuning and cost management.</span></p></li></ul></div><div><ul><li><p><span>Python and SQL for pipeline development and testing.</span></p></li></ul></div><div><ul><li><p><span>Change data capture and event processing, including ordering, replay and schema change.</span></p></li></ul></div><div><ul><li><p><span>Azure, including Databricks, ADLS and private network connectivity.</span></p></li></ul></div><div><ul><li><p><span>GitHub and CI/CD for data workloads, using GitHub Actions or an equivalent system.</span></p></li></ul></div><div><ul><li><p><span>Data quality, reconciliation, monitoring and production incident response.</span></p></li></ul></div><div><ul><li><p><span>Experience building multi-tenant, secure data platforms, including tenant isolation, access control and data protection.</span></p></li></ul><p><span>Desirable</span></p></div><div><ul><li><p><span>Databricks Lakeflow, Auto CDC, Declarative Automation Bundles and DQX, or the Snowflake equivalents: Dynamic Tables, Streams and Tasks, Snowpark, Snowflake CLI deployments and Data Metric Functions.</span></p></li></ul></div><div><ul><li><p><span>Debezium, Kafka Connect or Confluent Kafka. This is the current ingestion path. It may be replaced.</span></p></li></ul></div><div><ul><li><p><span>Apache Airflow, or an equivalent workflow orchestrator.</span></p></li></ul></div><div><ul><li><p><span>Kafka or Spark Structured Streaming, Apache Iceberg or Delta Sharing, and dbt for analytical models on curated data.</span></p></li></ul></div><div><ul><li><p><span>Private markets data: valuations, funds, portfolio companies or capital activity.</span></p></li></ul></div><div><ul><li><p><span>Comfortable working directly with client technical teams, and collaborating across field engineering, product and other stakeholders.</span></p></li></ul></div>

<p><strong>73 STRINGS 公司简介：</strong></p><p>73 Strings 是一个创新平台，为私募资本行业提供全面的数据提取、监控和估值解决方案。该公司的AI驱动平台简化了另类投资的中台流程，只需点击一下即可实现无缝数据结构化和标准化、监控和公允价值估算。73 Strings 为全球客户提供各类策略服务，包括私募股权、成长型股权、风险投资、基础设施和私人信贷。</p><p>我们2025年的5500万美元B轮融资是该行业规模最大的一轮融资，由高盛(Goldman Sachs)领投，Golub Capital和Hamilton Lane跟投，并获得黑石集团(Blackstone)、富达国际战略投资部(Fidelity International Strategic Ventures)和Broadhaven Ventures的持续支持。<br><br><span>关于这个职位</span></p><div><p><span>我们正在招聘一名高级数据工程师，负责构建和维护支持估值和监控的管道、集成和数据仓库。</span></p></div><div><p><span>您将负责从源数据捕获到转换、对账和客户交付的生产管道，并将它们置于GitHub上，实现自动化测试和CI/CD。当前平台从关系型系统中捕获变更数据，在Azure Databricks上处理，然后交付到Snowflake、Microsoft SQL Server和Databricks。捕获方法可能会改变。复制的客户管道正被元数据驱动的组件取代，包括数据契约、质量规则和数据谱系。</span></p></div><p></p><p><span>您的工作职责</span></p><div><ul><li><p><span>帮助重新定义平台的架构，涵盖摄取、处理和交付，使其稳定、安全且足够快速，以支持业务的高级用例。</span></p></li></ul></div><div><ul><li><p><span>构建和运行来自数据库、API、事件流和半结构化源的批处理和流处理管道。</span></p></li></ul></div><div><ul><li><p><span>实现变更数据捕获和增量加载，包括排序、删除、重放和缓慢变化维度。</span></p></li></ul></div><div><ul><li><p><span>构建勋章数据集和维度模型，并将它们交付到Snowflake、Microsoft SQL Server和Databricks。</span></p></li><li><p><span>在发布前应用数据契约、对账和行级隔离。</span></p></li></ul></div><div><ul><li><p><span>拥有GitHub工作流和CI/CD，包括测试、审核、环境推广和代码部署。</span></p></li></ul></div><div><ul><li><p><span>调查生产数据故障，并将产品、估值和面向客户团队的需求转化为可操作的管道。</span></p></li></ul><p><span>任职要求</span></p></div><div><ul><li><p><span>10年以上生产系统数据工程经验。</span></p></li></ul></div><div><ul><li><p><span>以Snowflake或Databricks作为主要平台，包括建模、性能调优和成本管理。</span></p></li></ul></div><div><ul><li><p><span>使用Python和SQL进行管道开发和测试。</span></p></li></ul></div><div><ul><li><p><span>变更数据捕获和事件处理，包括排序、重放和架构变更。</span></p></li></ul></div><div><ul><li><p><span>Azure，包括Databricks、ADLS和私有网络连接。</span></p></li></ul></div><div><ul><li><p><span>GitHub和数据工作负载的CI/CD，使用GitHub Actions或等效系统。</span></p></li></ul></div><div><ul><li><p><span>数据质量、对账、监控和生产事件响应。</span></p></li></ul></div><div><ul><li><p><span>构建多租户安全数据平台的经验，包括租户隔离、访问控制和数据保护。</span></p></li></ul><p><span>优先考虑</span></p></div><div><ul><li><p><span>Databricks Lakeflow、Auto CDC、Declarative Automation Bundles和DQX，或Snowflake的等效功能：Dynamic Tables、Streams和Tasks、Snowpark、Snowflake CLI部署和Data Metric Functions。</span></p></li></ul></div><div><ul><li><p><span>Debezium、Kafka Connect或Confluent Kafka。这是当前的摄取路径，可能会被替代。</span></p></li></ul></div><div><ul><li><p><span>Apache Airflow或等效的工作流编排器。</span></p></li></ul></div><div><ul><li><p><span>Kafka或Spark Structured Streaming、Apache Iceberg或Delta Sharing，以及用于整理数据分析模型的dbt。</span></p></li></ul></div><div><ul><li><p><span>私募市场数据：估值、基金、投资组合公司或资本活动。</span></p></li></ul></div><div><ul><li><p><span>能够直接与客户技术团队合作，并与现场工程、产品和其他利益相关者协作。</span></p></li></ul></div>

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