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JOB DETAILS

Data Engineer Tech Lead

CompanyQuicklizard
LocationPetah Tikva
Work ModeOn Site
PostedSeptember 6, 2026
About The Company
Quicklizard is an AI-powered dynamic pricing platform for retailers and brands. It automates pricing decisions across 100% of a product catalog, helping retail and pricing teams replace manual spreadsheets, respond to competitor price changes in real time, and protect margins at scale. How it works: Quicklizard pairs AI price-elasticity modeling with live competitive market data to recommend — or automatically set - the optimal price for every product across every channel. Key capabilities: • AI-driven price elasticity modeling • Real-time competitive price monitoring and automated response • Glass Box AI - fully transparent, explainable pricing, so teams can see exactly why each price is set • Omnichannel price alignment across eCommerce, marketplaces, and physical stores • 100% catalog coverage from day one Quicklizard is used by leading global retailers and brands, including Sephora, Samsung, IKEA, Elkjøp, Whirlpool, and Logitech. Customers report an average 15% revenue lift and an 11% profit increase in the first year.
About the Role

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 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.

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
  • Lead technically: review designs and code, mentor engineers, and raise the bar for the data org

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
  • 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.



Requirements

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Key Skills
Data EngineeringSparkPythonGoSQLBigQueryAWSGCPETL/ELTData ArchitectureKafkaKubernetesTerraformAirflowDistributed SystemsAI/LLM Integration
Categories
TechnologyData & AnalyticsSoftwareEngineeringManagement & Leadership
Job Information
📋Core Responsibilities
You will architect and scale end-to-end data pipelines while leading technical direction and mentoring a team of data engineers. The role involves owning data lake design, ensuring data quality, and collaborating with cross-functional teams to expose data through APIs.
📋Job Type
full time
📊Experience Level
5-10
💼Company Size
97
📊Visa Sponsorship
No
💼Language
English
🏢Working Hours
40 hours
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