Data Engineer (m/f/d)
Role Overview
Build and scale a modern data platform in a consumer-facing B2C environment.
Work with Product, Marketing, Analytics, and Data teams on data products that support customer analytics, engagement measurement, attribution, experimentation, and business intelligence.
The role is a six-month extendable contract and is remote.
Key Skills for This Role
Full Job Posting
Role overview
Build and scale a modern data platform in a consumer-facing B2C environment.
Work with Product, Marketing, Analytics, and Data teams on data products that support customer analytics, engagement measurement, attribution, experimentation, and business intelligence.
The role is a six-month extendable contract and is remote.
Responsibilities
- Design, develop, and maintain scalable Python and SQL ETL/ELT processes.
- Build pipelines ingesting and processing web, mobile, marketing, and customer-platform data.
- Ensure data is available, accurate, and accessible for analytics and reporting.
- Analyze large customer and event-level datasets to generate business insights.
- Validate event capture and make event data available for downstream analytics.
- Integrate and manage marketing, acquisition, and advertising data pipelines.
- Help stakeholders understand customer acquisition performance and marketing ROI.
Required experience
- At least 3 years of data engineering experience.
- Strong SQL and Python proficiency and hands-on experience building and maintaining ETL/ELT pipelines.
- Experience with B2C, customer, consumer, product, or marketing data.
- Experience with a cloud data platform such as BigQuery, Snowflake, or Redshift.
- Understanding of customer data models, engagement analytics, conversion funnels, retention metrics, and customer lifecycle analysis.
- Experience integrating web, mobile, and marketing platforms.
- Familiarity with version control, testing frameworks, and software engineering best practices.
Preferred experience
- GA4, including BigQuery Export; Snowplow event tracking; modern transformation frameworks such as dbt.
- Docker, Kubernetes, or cloud-native deployment environments.
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