Senior Data Engineer

Job Description

Senior Data Engineer (B2B)

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Permanent, Direct Hire

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100% Remote within the US

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The Role

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As a Senior Data Engineer (B2B), you will own the development, maintenance, and evolution of data pipelines supporting B2B clients and capabilities. You will work across client teams, Data Engineering, and Product to translate complex B2B marketing and business requirements into reliable, scalable data solutions.

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This role will spend approximately 75–85% of its time supporting B2B client data needs, with the remaining time focused on improving and standardizing our broader B2B data capabilities.

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You will own B2B data pipelines end-to-end, including implementing approved modifications to downstream pipelines and data models when necessary to meet client requirements. This role also owns the data layer for measurement and scoring programs. Measurement methodology and analysis design are owned by strategy and analytics partners, with this role building the datasets, pipelines, and validation tooling that support them. You will be expected to understand how changes flow through the broader data ecosystem, evaluate their impact, and implement solutions that preserve data quality and maintainability.

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As you identify common requirements across B2B clients, you will help turn those patterns into standardized models, reusable components, and scalable B2B data capabilities that reduce repeated custom development.

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You Must Have

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• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.

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• 5+ years of experience in data engineering, analytics engineering, or a related field, with experience independently owning production data pipelines and data models.

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• Advanced proficiency in SQL and BigQuery, along with intermediate to advanced programming skills in Python.

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• Strong experience with dbt or an equivalent transformation and orchestration framework including data modeling, testing, documentation, and reusable development patterns.

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• Strong understanding of data warehousing, dimensional modeling, data transformation, and data quality principles.

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• 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.

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• 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.

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• Experience integrating data across multiple B2B systems and reconciling differences in identifiers, entities, lifecycle definitions, business processes, and data structures.

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• Experience within an advertising agency, consulting organization, or other multi-client environment.

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• Experience with lead-to-account matching and identity resolution, including reconciling person-level and account-level data across systems.

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• 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.

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Nice to Have

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• Experience with platforms across the B2B ecosystem

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• Experience with multi-touch attribution and sourced/influenced pipeline measurement, including per-opportunity attribution models and credit window logic.

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• Experience building B2B measurement models that connect marketing activity and media investment to account engagement, opportunities, pipeline, and revenue.

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• 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.

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• Experience building reusable or configuration-driven data pipelines that support multiple clients while allowing controlled client-specific customization.

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• Experience with data observability, lineage, metadata management, or data quality platforms.

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• 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.

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• Experience with audience syndication and reverse ETL patterns, including platform audience APIs (DSP, LinkedIn, Meta) or tools such as Hightouch or Census.

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• Experience managing BigQuery cost and performance across multiple clients, including partitioning, clustering, and query cost discipline.

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• 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