Data Engineer

Description

About Quicklizard

Quicklizard is a dynamic pricing platform used by leading retailers, marketplaces, and e-commerce brands worldwide. Our engine ingests sales, competitor, inventory, and cost data and turns it into real-time pricing recommendations - processing billions of records a day across multi-region pipelines that never stop running.

The Role

We're looking for a Data Engineer to own our data architecture end-to-end. You'll design, build, and scale the pipelines that power every pricing decision we make - from raw ingestion through our data lake to the analytics and BI layers our customers rely on. This is a hands-on leadership role: you'll set technical direction, drive architectural decisions, and mentor a team of data engineers, while still writing code and owning delivery.

What You'll Do

• Architect and build large-scale batch and streaming ETL/ELT pipelines
• Own data lake design, table modeling, and partitioning strategy across billion-row datasets
• Drive query performance and cloud cost optimization across AWS and GCP
• Establish data quality, observability, and reliability standards - freshness, correctness, and SLAs
• Partner with backend, product, and data science teams to expose data through internal and customer-facing APIs

Our Stack

Spark / EMR · Airflow · BigQuery · PostgreSQL & Aurora · Kafka · RabbitMQ · Elasticsearch · Go · Python · AWS · GCP · Kubernetes · Terraform

What We're Looking For

• 5+ years in data engineering, with real production experience at scale (terabytes+, billions of rows)
• Deep SQL and strong distributed-processing experience (Spark or equivalent)
• Strong Python and/or Go
• Hands-on experience with cloud data warehouses (BigQuery, Snowflake, Redshift) and orchestration tooling
• AI-first mindset - you actively work with AI coding tools (Claude Code, Cursor, Copilot) and LLM-based agents as part of your day-to-day, and look for opportunities to automate and accelerate engineering work with them
• Experience building or supporting AI/LLM-driven data products - pipelines that feed models, agents, or ML systems
• Product mindset: you care why the data is being used, not just that the job finished green

Nice to have: streaming architectures, cost/FinOps ownership, multi-region or multi-cloud systems, e-commerce or pricing domain experience, MCP servers or agentic tooling.