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Open nowPosted 12 hours ago

Senior Data Engineer

Jobgether4,270 open roles

Where
US
Work mode
Remote
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Your applicationOpen nowSenior Data EngineerJobgether · US

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Jobgether's own form.

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The clock on this job

Early applications get read.

8.0% of postings close within 7 days. Measured by our own scanner across the market. Jobgether postings stay open a median of 5 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.5%3 days
  3. 8.0%7 days
  4. 15.0%14 days
  5. 34.1%30 days
This job: posted 12 hours ago

Jobgether median: 5 days open

The posting

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in United States.

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.

Accountabilities:

  • 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.
  • 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.
  • Company-paid medical, dental, and vision insurance.
  • Retirement savings plan with company matching.
  • Flexible spending accounts.
  • Generous paid parental leave and paid time off.
  • Winter Wonder Week, providing a company-wide paid week off at the end of the year.
  • Remote work stipend to support a productive home office.
  • Benefits and resources supporting physical, mental, and emotional well-being.
  • Parenting and childcare support.
  • Financial planning resources.
  • Competitive compensation and opportunities for career advancement.
  • Learning and development resources focused on professional growth.
  • Access to leading AI tools such as Claude and ChatGPT to support day-to-day work.
  • Remote-first working environment across the U.S. and Canada.
  • Company-wide gatherings twice a year to strengthen collaboration and team connections.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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