Analytics Engineer

4w1 month ago

Bringg

Full-time · $130,000 – $170,000

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 our 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 our fully established Medallion Data Architecture in Google BigQuery, using SQL, Python, and dbt to implement new data solutions and support strategic initiatives. You will own, optimize, and extend production-ready dbt data models across Bronze, Silver, and Gold layers to support new business use cases.

You will collaborate closely with Data Engineers, Data Scientists, and BizOps teams to ingest new data sources and transform them into analytical readiness. By managing our unified semantic layer and treating data as code, you will ensure a single source of truth that directly fuels Bringg's advanced analytics, machine learning projects, and GenAI operations.

This role offers the opportunity to work with cutting-edge AI-assisted development tools like Claude Code, GitHub Copilot, and Cursor in a serious, production-oriented environment. You will have the autonomy to make technical decisions without a defined playbook while promoting engineering best practices across the data organization.

Requirements

4+ years of experience in data analytics, BI development, or data engineering with strong proficiency in SQL
Production-grade dbt experience — 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, documentation
Hands-on experience with AI-assisted development tools (Claude Code, GitHub Copilot, Cursor)
Comfortable owning your 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 robust dbt data tests, defining model health scores, and maintaining comprehensive column-level documentation
Own and scale the unified dbt Semantic Layer to guarantee a single source of truth for business metrics used by internal operations, customer-facing analytics, and AI/ML initiatives
Bridge engineering and impact by collaborating with Data Engineers, Data Scientists, and BizOps teams 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 including version control, code reviews, and automated deployment
Support upcoming strategic initiatives by implementing new data solutions and maintaining robust data models
Use AI-assisted development tools (Claude Code, GitHub Copilot, Cursor) in a serious, production-oriented capacity

Benefits

Work with a fully established Medallion Data Architecture in Google BigQuery
Use cutting-edge AI-assisted development tools in a serious, production-oriented environment
Autonomy to make technical decisions and own your work independently
Collaborate with cross-functional teams including Data Engineers, Data Scientists, and BizOps