Senior Data Engineer
S
Spoiler AlertCPG marketplace
U.S. RemoteFull-TimeSenior
Salary140,000 - 175,000 USD per year
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Job Details
- Experience
- 5–8+ years of data engineering experience
- Required Skills
- SQLData engineeringData modelingdbt
Requirements
- Have 5–8+ years of data engineering experience; experience at a B2B SaaS, marketplace, or data products company is preferred.
- Bring hands-on expertise across modern data-stack pipeline tooling, workflow orchestration, transformations, and BI tools with semantic layers.
- Have experience with Estuary, Astronomer Airflow, dbt, and Holistics, the tools used by the team.
- Have strong SQL and data modeling foundations.
- Have experience owning data quality end-to-end, including testing and observability frameworks and tools.
- Integrate AI productively into development workflows.
- Demonstrate accountability for systems and themes, seeing work through and communicating clearly.
- Collaborate with data analysts, product engineers, and non-technical stakeholders.
- Experience with marketplace or transaction-platform data, applied AI/ML pipeline work, customer-facing analytics products, food and beverage or CPG, or LLM tooling in data workflows is a differentiator.
Responsibilities
- Architect, build, and maintain infrastructure that moves and transforms marketplace data.
- Own the analytical data warehouse schema for internal analytics, aggregate analytics, and customer-facing reporting.
- Define data quality standards and develop monitoring and testing practices.
- Document architectural decisions and tradeoffs, including infrastructure for AI/ML product applications.
- Contribute code in pipeline logic, SQL, and data modeling, and provide meaningful code reviews.
- Debug data issues and drive root-cause work to prevent recurrence.
- Own data systems and roadmap themes from design through production and iteration.
- Partner with Product, Data, and Engineering teams to support reliable decision-making with data.
- Identify and advocate for data infrastructure investments across Product, Data, Engineering, and Operations.
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