Senior Staff Data Engineer
New
Based in United StatesFull-TimeStaff
Salary$191,000 – $287,000
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Job Details
- Experience
- 8+ years
- Required Skills
- PythonSQLETLSnowflakeAirflowdbtDatabricksPySpark
Requirements
- 8+ years of experience in data engineering with strong exposure to staff or principal-level responsibilities.
- Deep technical expertise in Python, SQL, PySpark, dbt, and workflow orchestration tools such as Airflow.
- Strong experience with modern data platforms such as Databricks, Snowflake, or equivalent cloud data ecosystems.
- Proven track record of building and scaling reliable ETL pipelines and data integration systems in production environments.
- Experience owning ambiguous, end-to-end technical problems and driving solutions independently in fast-paced environments.
- Strong understanding of data reliability, pipeline observability, and production-grade data engineering practices.
- Demonstrated interest in AI-assisted engineering and experience using AI tools or agentic workflows to improve productivity.
- Ability to work effectively with messy, real-world enterprise data and deliver pragmatic, scalable solutions.
- Strong collaboration and influence skills, with experience working across engineering, product, and business teams.
- Prior experience in retail, grocery, or supply chain domains is a plus but not required.
Responsibilities
- Architect, build, and maintain scalable data systems and pipelines that power core product and AI-driven workflows from ingestion to production delivery.
- Own end-to-end resolution of complex and ambiguous data engineering problems, delivering robust solutions without predefined specifications.
- Define data architecture standards, engineering patterns, and reusable abstractions to improve integration speed and system consistency.
- Improve data reliability through monitoring, alerting, self-healing pipelines, and resilience strategies for real-world, messy enterprise data.
- Drive adoption of modern AI-augmented engineering workflows and tools to accelerate development and improve efficiency.
- Provide technical leadership through architecture reviews, code reviews, and mentoring of other engineers across teams.
- Partner closely with Product, ML, and customer-facing teams to align data systems with business and customer needs.
- Continuously evaluate and enhance data platform performance, scalability, and maintainability across evolving requirements.
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