高级数据工程师（B2B）
Senior Data Engineer (B2B)
你将负责
使用BigQuery、dbt、Python和编排框架，为Wpromote的B2B客户设计、开发、部署和持续支持数据流水线。
与客户团队和技术利益相关者合作，将B2B营销、销售、度量和报告需求转化为可靠的数据解决方案。
在整个数据生命周期中管理客户特定的需求，包括在需要时对下游流水线、转换和数据模型进行批准的修改。
在B2B技术生态系统中集成和标准化数据，包括CRM、营销自动化、ABM和意图、归因、增强、销售参与、付费媒体、网络分析以及第一方产品使用和遥测数据。
构建数据模型，将匿名和已知的参与、潜在客户、联系人、账户、活动、机会、营销触点和销售活动连接起来，以支持从初始参与到管道和收入的账户级度量。
理解上下游依赖关系，排查复杂的生产问题，并确保变更保持数据质量、可靠性和可扩展性。
通过自动化测试、验证、对账、监控和可观测性来加强数据质量。
识别B2B客户之间的重复需求，并将其转化为可重用的模型、框架和配置驱动的解决方案。
为Wpromote的标准B2B数据架构做出贡献，包括账户解析、生命周期阶段、漏斗定义和营销到收入度量的通用方法，同时保持对客户特定需求的灵活性。
使用AI辅助开发工具加速开发、调试、测试、文档和代码库探索，同时对生产代码保持责任。
构建激活流水线，将数据重新推送到运营系统，包括将账户分段、评分和生命周期阶段同步到广告平台，将丰富数据和任务写回CRM，并保持平台受众与仓库状态一致。
调查和验证第三方和供应商数据馈送的行为，包括交付频率、重述、保留和模式语义，并设计在馈送行为与文档不一致时仍能保持正确的流水线。
构建分析数据集，支持评分模型的校准和验证，例如回归分析等
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You Will Be
Owning the design, development, deployment, and ongoing support of data pipelines for Wpromote’s B2B clients using BigQuery, dbt, Python, and orchestration frameworks.
Partnering with client teams and technical stakeholders to translate B2B marketing, sales, measurement, and reporting requirements into reliable data solutions.
Managing client-specific requirements throughout the data lifecycle, including making approved modifications to downstream pipelines, transformations, and data models when required.
Integrating and normalizing data across the B2B technology ecosystem, including CRM, marketing automation, ABM and intent, attribution, enrichment, sales engagement, paid media, web analytics, and first-party product usage and telemetry data.
Building data models that connect anonymous and known engagement, leads, contacts, accounts, campaigns, opportunities, marketing touchpoints, and sales activity to support account-level measurement from initial engagement through pipeline and revenue.
Understanding upstream and downstream dependencies, troubleshooting complex production issues, and ensuring changes maintain data quality, reliability, and scalability.
Strengthening data quality through automated testing, validation, reconciliation, monitoring, and observability.
Identifying recurring requirements across B2B clients and converting them into reusable models, frameworks, and configuration-driven solutions.
Contributing to Wpromote’s standard B2B data architecture, including common approaches to account resolution, lifecycle stages, funnel definitions, and marketing-to-revenue measurement while maintaining flexibility for client-specific requirements.
Using AI-assisted development tools to accelerate development, debugging, testing, documentation, and codebase exploration while maintaining accountability for production code.
Building activation pipelines that push data back out to operational systems, including syncing account segments, scores, and lifecycle stages to ad platforms, writing enriched data and tasks back to CRM, and keeping platform audiences current with warehouse state.
Investigating and validating the behavior of third-party and vendor data feeds, including delivery cadence, restatement, retention, and schema semantics, and designing pipelines that remain correct when feeds behave differently than documented.
Building analytical datasets that support scoring model calibration and validation, such as regression inputs from CRM history, holdout construction, lift analysis, and feedback loops from sales dispositioning.
You Must Have
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
5+ years of experience in data engineering, analytics engineering, or a related field, with experience independently owning production data pipelines and data models.
Advanced proficiency in SQL and BigQuery, along with intermediate to advanced programming skills in Python.
Strong experience with dbt or an equivalent transformation and orchestration framework including data modeling, testing, documentation, and reusable development patterns.
Strong understanding of data warehousing, dimensional modeling, data transformation, and data quality principles.
Hands-on data engineering or analytics experience with CRM and at least two other areas of the B2B technology ecosystem, such as marketing automation, ABM/intent, attribution, or paid media platforms.
Strong understanding of B2B data models and customer journeys, including lead/contact-to-account relationships, lifecycle and funnel stages, opportunity and pipeline data, campaign membership, marketing touchpoints, and the challenges of connecting marketing activity to revenue.
Experience integrating data across multiple B2B systems and reconciling differences in identifiers, entities, lifecycle definitions, business processes, and data structures.
Strong problem-solving and communication skills, including the ability to navigate ambiguous client requirements, evaluate technical tradeoffs, and communicate solutions to technical and non-technical stakeholders.
Experience within an advertising agency, consulting organization, or other multi-client environment.
Experience with lead-to-account matching and identity resolution, including reconciling person-level and account-level data across systems.
Experience reconstructing historical state from CRM and marketing data, such as lifecycle and opportunity stage history, snapshot-based feeds, and changes in account ownership or campaign membership over time.
Nice to Have
Experience with platforms across the B2B ecosystem, including:
CRM & Sales: Salesforce Sales Cloud, HubSpot
Marketing Automation: Marketo, Pardot (Marketing Cloud Account Engagement), Eloqua
ABM & Intent: 6sense, Demandbase
B2B Measurement & Attribution: Octane11, Adobe Marketo Measure (formerly Bizible)
Data Enrichment & Prospecting: ZoomInfo
Sales & Conversational Engagement: Salesloft, Qualified, Drift
Content & Buyer Engagement: PathFactory
Experience with multi-touch attribution and sourced/influenced pipeline measurement, including per-opportunity attribution models and credit window logic.
Experience building B2B measurement models that connect marketing activity and media investment to account engagement, opportunities, pipeline, and revenue.
Experience working with historical CRM and marketing data, including changes in lifecycle status, opportunity stages, campaign membership, account ownership, and other business events over time.
Experience building reusable or configuration-driven data pipelines that support multiple clients while allowing controlled client-specific customization.
Experience with data observability, lineage, metadata management, or data quality platforms.
Familiarity with orchestration technologies such as Airflow, Dagster, AWS Glue, or Azure Data Factory, along with modern software engineering practices including Git, code review, CI/CD, and testing.
Experience with audience syndication and reverse ETL patterns, including platform audience APIs (DSP, LinkedIn, Meta) or tools such as Hightouch or Census.
Experience managing BigQuery cost and performance across multiple clients, including partitioning, clustering, and query cost discipline.
Hands-on experience using AI-assisted software engineering tools such as Claude Code, OpenAI Codex, OpenCode, Cursor, GitHub Copilot, or similar tools, with the engineering judgment to review, test, and validate AI-generated solutions.
What Success Looks Like
Success in this role will center around three areas:
Client Ownership: You will become a trusted technical owner for Wpromote’s B2B data pipelines, independently translating complex client requirements into reliable solutions and confidently managing changes across upstream and downstream systems.
Quality: You will improve the reliability of our B2B data through stronger testing, validation, monitoring, documentation, and observability, while proactively identifying and addressing recurring data issues.
Scale: You will recognize common patterns across B2B clients and help turn them into standardized, reusable capabilities, reducing repeated custom development while preserving the flexibility needed to support meaningful client-specific requirements.