Senior Data Engineer

United StatesRemoteFull-timesenior

Data EngineerData

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Quick Summary

Requirements Summary

7+ years of experience building and operating production-grade data engineering systems at scale. Strong expertise in Python and modern data engineering principles,

Technical Tools

Data EngineerData

This senior individual contributor role sits at the intersection of data engineering, platform architecture, and AI-native tooling. You will build and scale reliable data infrastructure supporting a large, data-intensive digital business and millions of users. Beyond traditional pipelines, you will design systems that automate how data engineering work is generated, tested, monitored, and maintained. You will also help operationalize machine learning workflows and strengthen the reliability of shared data assets. Working closely with analysts, data scientists, product teams, and analytics engineers, you will translate business needs into scalable technical solutions. This is a remote-first opportunity across the U.S. and Canada, with a strong focus on innovation, ownership, and practical use of AI.

Build and scale reliable, high-performance data pipelines for ingestion into Snowflake, supporting a large-scale e-commerce and digital platform.
Design and develop agentic systems that automate data engineering workflows, including pipeline generation, testing, maintenance, and other repetitive engineering tasks.
Build and maintain machine learning pipelines that enable data scientists to operationalize models and integrate them effectively with the broader data infrastructure.
Implement and improve data monitoring across complex, 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 spanning ingestion, transformation, analytics, and AI.
Identify opportunities to make data engineering more scalable through automation, reusable platforms, AI-assisted workflows, and improved engineering practices.
Contribute to the evolution of the data platform by balancing reliability, performance, maintainability, and emerging AI capabilities.

Requirements

~1 min read

7+ years of experience building and operating production-grade data engineering systems at scale.
Strong expertise in Python and modern data engineering principles, with a platform-oriented approach to solving recurring engineering challenges.
Proven experience building production AI or LLM systems, including RAG pipelines, agentic workflows, tool integrations, or comparable systems used by real users.
Strong proficiency with Airflow and dbt, including a deep understanding of data modeling, ETL principles, orchestration, and production data workflows.
Hands-on experience with AWS services such as EC2, S3, Lambda, and EKS, including provisioning and managing cloud-based data resources.
Experience with Snowflake or a comparable modern cloud data warehouse.
Ability to collaborate effectively with analysts, data scientists, product teams, and other technical stakeholders and translate their needs into reliable infrastructure.
Strong interest in AI and a demonstrated habit of using AI tools to improve productivity, engineering quality, and problem-solving.
Strong systems-thinking skills, with the ability to recognize repetitive data engineering work as an opportunity for automation and platform design.
Excellent communication, ownership, and problem-solving skills, with the ability to operate independently in a senior individual contributor capacity.