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.
עיקרי התפקיד
We're looking for a Data Engineering Tech Lead 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.
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
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
Proven technical leadership - mentoring engineers, owning architecture, driving decisions across teams
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.