Data Engineer

We're a company in the retail tech industry, working with data at large scale. Our platform is Snowflake-first, and AI is genuinely part of how this team works — in how we build pipelines, test them, and deliver to clients. Not a pilot, not a slide. You'll be expected to use it and to push it further.

You'll join a group, working alongside our engineering and analytics teams.

Responsibilities

• Design, build, and optimize data models and transformation logic in Snowflake, with real attention to performance and cost
• Build and maintain ELT pipelines orchestrated in Apache Airflow
• Own data quality end to end: testing, monitoring, alerting, root-cause analysis on production issues
• Apply AI and agent-based tooling to the engineering workflow — code generation, automated testing, pipeline automation
• Maintain the ClickHouse serving layer behind our customer-facing analytics
• Partner with analysts and product to turn business logic into reliable, maintainable data models

Requirements

• 2+ years hands-on data engineering
• Excellent SQL, including optimization of complex queries on large, complex datasets
• Hands-on experience with a cloud data warehouse — Snowflake strongly preferred
• Production experience with a data transformation framework (dbt or similar)
• Production experience with a workflow orchestrator (Airflow, Dagster or similar)
• Git, code review, and CI as normal practice
• Genuine bias toward using AI/LLM tooling in your own engineering work
• Ownership mindset — you chase a data issue to its root cause instead of patching the symptom

Advantages

• Snowflake depth: stored procedures, Snowpark, Streamlit in Snowflake, Cortex
• Building with LLM APIs, agent frameworks, or MCP
• ClickHouse or another OLAP engine
• AWS (S3, RDS/PostgreSQL, EC2)