Analytics Engineer

4w1 month ago

Bringg

Tel Aviv, IL · Full-time · ILS 400,000 – ILS 520,000

About this role

Bringg is the infrastructure behind delivery operations for some of the world's largest retailers, processing over 200 million orders annually through a smart, automated omnichannel platform. We are looking for an Analytics Engineer to maximize the potential of our data ecosystem and drive its future growth.

On a day-to-day basis, you will leverage a fully established Medallion Data Architecture in Google BigQuery, using SQL, Python, and dbt to implement new data solutions and support upcoming strategic initiatives. You will also own the unified semantic layer, treating data as code to maintain a single source of truth that fuels advanced analytics, machine learning projects, and GenAI operations.

You will collaborate closely with Data Engineers, Data Scientists, and BizOps teams to ingest new data sources and transform them into analytical readiness. The environment prioritizes engineering rigor with version control, code reviews, and automated deployment across the data pipeline.

This role offers the opportunity to directly impact how deliveries land on time for millions of customers and to shape the data foundation for Bringg's next generation of AI-driven logistics. You will work with cutting-edge tools in a production-grade setting where your technical decisions drive real business outcomes.

Requirements

4+ years of experience in data analytics, BI development, or data engineering with strong SQL proficiency and a track record building or maintaining data pipelines
Production-grade dbt experience including modeling, testing, and deploying modular frameworks at scale
Deep experience writing and performance-tuning complex queries in BigQuery
Proficiency in Python for data manipulation, scripting, or analysis
Solid engineering fundamentals: Git, query optimization, code reviews, and documentation
Hands-on experience with AI-assisted development tools (Claude Code, GitHub Copilot, Cursor) — used seriously, not experimentally
Comfortable owning work independently and making technical decisions without a defined playbook

Responsibilities

Leverage and scale the Medallion Pipeline by owning, optimizing, and extending production-ready dbt data models across Bronze, Silver, and Gold layers in Google BigQuery
Ensure data quality and governance by implementing dbt data tests, defining model health scores, and maintaining column-level documentation
Own and scale the unified dbt Semantic Layer to guarantee a single source of truth for internal business ops, customer-facing analytics, and AI/ML initiatives
Bridge engineering and impact by collaborating with Data Engineers, Data Scientists, and BizOps to ingest new data sources and transform them into analytical readiness
Promote best practices by writing clean, modular, performance-tuned SQL and treating data pipelines with a software engineering mindset

Benefits

Work with a fully established Medallion Data Architecture in Google BigQuery at scale
Opportunity to fuel advanced analytics, machine learning, and GenAI operations directly
Collaboration with Data Engineers, Data Scientists, and BizOps teams on strategic initiatives
Access to and usage of cutting-edge AI-assisted development tools like Claude Code and GitHub Copilot