Senior AI Data Engineer - Marketing Analytics
New
Washington StateFull-TimeSenior
Salary130,000 - 140,000 USD per year
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Job Details
- Experience
- 4+ years of relevant experience OR Master’s degree in a related field with 3+ years of relevant experience OR Equivalent professional experience. 2+ years of hands-on experience in an analytics engineering or data engineering role supporting business stakeholders. 4–5+ years Advanced Python skills. 3+ years Experience designing and building data models. 2+ years supporting performance marketing, growth, or digital product analytics use cases (preferred)
- Required Skills
- PythonSQLSpark
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical field with 4+ years of relevant experience OR Master’s degree in a related field with 3+ years of relevant experience OR Equivalent professional experience.
- 2+ years of hands-on experience in an analytics engineering or data engineering role supporting business stakeholders.
- Strong proficiency in SQL for analytics and data modeling.
- Advanced Python skills for data transformation, automation, and pipeline development (4–5+ years).
- Experience designing and building data models to support analytics and reporting use cases (3+ years).
- Experience working with large-scale data pipelines handling logs and/or event-streaming data.
- Hands-on experience with cloud-based data platforms and modern data tools (e.g., Spark, workflow orchestration tools); cloud experience strongly preferred.
Responsibilities
- Partner closely with analysts, data scientists, engineers, and business stakeholders to understand analytics and reporting needs and enable self-service data access.
- Build subject matter expertise in relevant business and data domains to ensure accurate interpretation and application of data.
- Identify, analyze, and document multi-structured data from diverse sources to support analytics and machine learning requirements.
- Design and maintain scalable data models, dataflows, and abstractions that evolve with changing business needs.
- Architect, build, and optimize automated data pipelines that transform raw logs and event data into clean, enriched, analytics-ready datasets.
- Apply modern analytics and data engineering best practices using object-oriented and modular approaches.
- Develop reusable frameworks, standardized components, and new data capabilities to improve system scalability and efficiency.
- Enable and enhance performance marketing strategies by delivering robust, reliable data products and insights.
- Ensure high standards for data quality, security, and reliability across all solutions.
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