Data Engineer

Arhaus was founded in 1986 on a simple idea: Furniture and décor should be sustainably sourced, lovingly made, and built to last. Today, we partner with artisans around the world who share our vision, creating beautiful, heirloom-quality pieces that can be used—and loved—for generations.

Description:

The Data Engineer is responsible for designing, building, and optimizing scalable data platforms and AI-driven solutions. This role plays a critical part in advancing a modern, Snowflake-first data architecture, with a strong emphasis on AI agent implementation, data pipeline automation, and advanced analytics enablement.

This individual will partner cross-functionally with business, analytics, and technology teams to deliver high-impact data and AI capabilities that support business decision-making and operational efficiency.

Essential Duties & Responsibilities:

This role is focused on modern data engineering and AI integration within a cloud-based ecosystem. Key responsibilities include:

Data Engineering & Snowflake:

Design, develop, and optimize data pipelines, models, and workflows using Snowflake

Manage large-scale data ingestion, transformation, and processing pipelines (ETL/ELT)

Ensure data quality, reliability, performance, and scalability across platforms

AI & Advanced Data Solutions:

Develop and implement AI agents and AI-driven workflows using Snowflake (Cortex)

Integrate structured and unstructured data sources to support AI use cases such as automation and document processing

Build orchestration logic for AI pipelines and workflows

Python & Integration Development:

Build and maintain data pipelines and automation solutions using Python

Develop integrations with APIs, external systems, and AI services

Implement data transformation, validation, and processing logic

Data Modeling & Analytics Enablement:

Design and maintain scalable data models aligned with data warehousing best practices (OLAP/EDW)

Write and optimize SQL queries across Snowflake and SQL Server environments

Support downstream analytics, reporting, and business intelligence initiatives

Data Integration & Automation:

Build scalable ingestion frameworks, including batch and API-based solutions

Automate workflows to support data movement and AI processing

Integrate enterprise systems into a unified data ecosystem

Data Governance & Collaboration:

Support data governance, security, and compliance best practices

Partner with analysts, business stakeholders, and leadership to translate requirements into technical solutions

Document data architecture, pipelines, and AI workflows

Requirements:

3+ years of experience working with Snowflake

5+ years of experience with SQL and SQL Server

5+ years of experience in data engineering, data modeling, or data pipeline development

2+ years of experience with Python programming

2+ years of experience developing or implementing AI/ML solutions or AI agents

Strong experience with ETL/ELT processes, API integrations, and data pipeline architecture

Solid understanding of data warehousing concepts (OLAP, EDW)