Note: The job is a remote job and is open to candidates in USA. Dale WorkForce Solutions is recruiting for a Senior Data Engineer (B2B) to own the development, maintenance, and evolution of data pipelines supporting B2B clients and capabilities. The role partners with client teams, Data Engineering, and Product to build scalable data solutions, measurement datasets, activation pipelines, and standardized B2B data capabilities.
Responsibilities
- Owning the design, development, deployment, and ongoing support of data pipelines for 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 the 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
Skills
- 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
- 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
Benefits
- 100% Remote
Company Overview
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