Data Engineer Tech Lead

Quicklizard; United States; On-site; USD 140,000 - 200,000; Full time

Job summary

Quicklizard is seeking a Data Engineering Tech Lead to own end-to-end data architecture, design scalable pipelines, and mentor a team of data engineers while contributing code. The role spans ingestion, data lake, analytics, and API data exposure across AWS and GCP.

You will set technical direction and drive architectural decisions to support pricing decisions at scale. The ideal candidate brings 5+ years in data engineering, deep SQL, Python/Go, and hands-on experience with cloud data

Qualifications

• Minimum 5 years of production data engineering experience.
• Deep SQL expertise and distributed processing with Spark or equivalent.
• Proficient in Python and/or Go.
• Hands-on experience with cloud data warehouses (BigQuery, Snowflake, Redshift) and orchestration tooling.
• Experience with AI tools/LLM-driven data products is a plus.
• Proven technical leadership, mentoring, and architecture ownership.

Responsibilities

• 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, SLAs).
• Partner with backend, product, and data science teams to expose data via APIs.
• Lead technically: review designs and code, mentor engineers, raise the bar for the data org.

Skills

Deep SQL

Distributed processing

Python

Go

Cloud data warehouses

BigQuery

Snowflake

Redshift

Spark

Airflow

PostgreSQL/Aurora

Kubernetes

Terraform

Tools

Spark

Airflow

BigQuery

Snowflake

Redshift

PostgreSQL/Aurora

Kafka

RabbitMQ

Elasticsearch

AWS

GCP

Kubernetes

Terraform

Job 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 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.