Senior Data Engineer
New
J
JobgetherE-commerce data
This is a remote-first opportunity across the U.S. and Canada.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years of experience building and operating production-grade data engineering systems at scale.
- Required Skills
- AWSPythonSnowflakeAirflowData engineeringdbt
Requirements
- Have 7+ years of experience building and operating production-grade data engineering systems at scale.
- Bring strong expertise in Python and modern data engineering principles, with a platform-oriented approach.
- Have production AI or LLM systems experience, including RAG pipelines, agentic workflows, tool integrations, or comparable systems used by real users.
- Have strong proficiency with Airflow and dbt, including data modeling, ETL principles, orchestration, and production data workflows.
- Have hands-on experience with AWS services such as EC2, S3, Lambda, and EKS, including provisioning and managing cloud-based data resources.
- Have experience with Snowflake or a comparable modern cloud data warehouse.
- Be able to collaborate with analysts, data scientists, product teams, and other technical stakeholders and translate their needs into reliable infrastructure.
- Demonstrate interest in AI and a habit of using AI tools to improve productivity, engineering quality, and problem-solving.
- Bring systems-thinking skills and recognize repetitive data engineering work as an opportunity for automation and platform design.
- Be able to operate independently in a senior individual contributor capacity.
Responsibilities
- Build and scale reliable, high-performance data pipelines for ingestion into Snowflake.
- Design and develop agentic systems to automate pipeline generation, testing, maintenance, and repetitive engineering tasks.
- Build and maintain machine learning pipelines to help data scientists operationalize models and integrate them with the data infrastructure.
- Implement and improve monitoring across multi-system user journeys to strengthen reliability, observability, and data quality.
- Collaborate with Analytics Engineers on data modeling and the reliability and scalability of shared data assets.
- Partner with product, analytics, data science, and ML teams to deliver end-to-end data solutions.
- Identify opportunities to scale data engineering through automation, reusable platforms, and AI-assisted workflows.
- Help evolve the data platform while balancing reliability, performance, maintainability, and emerging AI capabilities.
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