Senior Data Engineer (B2B)
New
W
WpromoteDigital Marketing
This position may be performed remotely in most states within the US, with some exclusionsFull-TimeSenior
Salary105,000 - 135,000 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLData modelingBigQuerydbtCRM
Requirements
- 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.
- Advanced proficiency in SQL and BigQuery.
- Intermediate to advanced programming skills in Python.
- Strong experience with dbt or an equivalent transformation and orchestration framework.
- Strong understanding of data warehousing, dimensional modeling, and data quality principles.
- Hands-on data engineering or analytics experience with CRM and at least two other B2B areas like marketing automation, ABM/intent, attribution, or paid media.
- Strong understanding of B2B data models, customer journeys, lead-to-account relationships, and marketing-to-revenue measurement.
- Experience integrating data across multiple B2B systems and reconciling differences in identifiers and business processes.
- Experience within an advertising agency, consulting organization, or other multi-client environment.
- Experience with lead-to-account matching, identity resolution, and reconstructing historical state from CRM/marketing data.
Responsibilities
- Own the design, development, deployment, and ongoing support of data pipelines for B2B clients using BigQuery, dbt, Python, and orchestration frameworks.
- Partner with client teams and technical stakeholders to translate B2B marketing, sales, measurement, and reporting requirements into reliable data solutions.
- Manage client-specific requirements throughout the data lifecycle, including modifications to pipelines, transformations, and data models.
- Integrate and normalize data across the B2B technology ecosystem including CRM, marketing automation, attribution, and paid media.
- Build data models that connect engagement, leads, contacts, accounts, and sales activity to support account-level measurement.
- Strengthen data quality through automated testing, validation, reconciliation, monitoring, and observability.
- Convert recurring client requirements into reusable models, frameworks, and configuration-driven solutions.
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