Senior Data Engineer - ML, DataOps & Snowflake (Remote)

Cimpress; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Cimpress, a remote‑first employer, seeks an experienced data engineer to design and maintain large data pipelines and modern data warehouses. You will leverage Python and SQL to build scalable ETL/ELT processes, optimize data modeling, and collaborate with BI teams using Looker.

You will work hands‑on with Snowflake, DBT, MLflow, SageMaker, and cloud platforms (AWS/Azure/GCP) while applying DataOps and MLOps practices in a dynamic, ambiguity‑tolerant environment.

Qualifications

• 3+ years of experience handling large data volumes and orchestrating and monitoring automated Batch & Near Real-Time ETL/data pipelines using CI/CD and Cloud Technologies, with preferred expertise in DBT or DBT Cloud.
• Strong programming skills in Python and SQL.
• Solid experience with MPP Data Warehouse systems such as Snowflake or Amazon Redshift, along with cloud platforms including AWS (Preferred), Azure, or GCP.
• Expertise in Data Modelling and Data Warehousing best practices, with a strong ability to adopt development best practices such as modularization, testing, and refactoring.
• Experience with Business Intelligence and reporting tools such as Looker is an added advantage.
• Hands‑on experience with Machine Learning (ML) and MLOps practices, including model training, versioning, deployment, monitoring, and lifecycle management using tools like MLflow, SageMaker, or Vertex AI.
• Ability to leverage AI tools in day‑to‑day data engineering tasks, such as using GitHub Copilot or Claude for code generation, debugging, query optimization, and pipeline development to improve productivity and efficiency.
• Exposure to Generative AI tools and frameworks (e.g., LangGraph, LangChain, OpenAI APIs).
• Curiosity to explore and implement evolving data engineering and Generative AI technologies.
• Understanding of modern practices like DataOps, MLOps or equivalent experience.
• Strong problem‑solving skills with a solid understanding of data structures, algorithms, and the ability to thrive in ambiguous environments with minimal oversight.

Responsibilities

• 3+ years of experience handling large data volumes and orchestrating and monitoring automated Batch & Near Real-Time ETL/data pipelines using CI/CD and Cloud Technologies, with preferred expertise in DBT or DBT Cloud.
• Strong programming skills in Python and SQL.
• Solid experience with MPP Data Warehouse systems such as Snowflake or Amazon Redshift, along with cloud platforms including AWS (Preferred), Azure, or GCP.
• Expertise in Data Modelling and Data Warehousing best practices, with a strong ability to adopt development best practices such as modularization, testing, and refactoring.
• Experience with Business Intelligence and reporting tools such as Looker is an added advantage.
• Hands‑on experience with Machine Learning (ML) and MLOps practices, including model training, versioning, deployment, monitoring, and lifecycle management using tools like MLflow, SageMaker, or Vertex AI.
• Ability to leverage AI tools in day‑to‑day data engineering tasks, such as using GitHub Copilot or Claude for code generation, debugging, query optimization, and pipeline development to improve productivity and efficiency.
• Exposure to Generative AI tools and frameworks (e.g., LangGraph, LangChain, OpenAI APIs).
• Curiosity to explore and implement evolving data engineering and Generative AI technologies.
• Understanding of modern practices like DataOps, MLOps or equivalent experience.
• Strong problem‑solving skills with a solid understanding of data structures, algorithms, and the ability to thrive in ambiguous environments with minimal oversight.

Skills

Python

SQL

Data Modeling

Tools

Snowflake

DBT

MLflow

SageMaker

Vertex AI

Looker

Job description

Cimpress, a remote‑first employer, seeks an experienced data engineer to design and maintain large data pipelines and modern data warehouses. You will leverage Python and SQL to build scalable ETL/ELT processes, optimize data modeling, and collaborate with BI teams using Looker.

You will work hands‑on with Snowflake, DBT, MLflow, SageMaker, and cloud platforms (AWS/Azure/GCP) while applying DataOps and MLOps practices in a dynamic, ambiguity‑tolerant environment.