Analytics Engineer

Bringg is the infrastructure behind delivery operations for some of the world's largest retailers. Every year, we process over 200 million orders through our smart, automated omnichannel platform experience. When it works, deliveries land on time. When it doesn't, customers feel it fast - and so do we. 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, support upcoming strategic initiatives, and maintain robust data models. 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. In this role, you will: Leverage & Scale the Medallion Pipeline: Own, optimize, and extend our production-ready dbt data models across Bronze, Silver, and Gold layers in Google BigQuery to support new business use cases. Ensure Data Quality & Governance: Implement and enforce robust dbt data tests to surface inconsistencies early, define model health scores, and maintain comprehensive column-level documentation. Own the Semantic Layer: Maintain and scale our unified dbt Semantic Layer, guaranteeing a single source of truth for business metrics utilized by internal business operations, customer-facing embedded analytics, and AI/ML initiatives. Bridge Engineering and Impact: Collaborate closely with Data Engineers, Data Scientists, and BizOps teams to ingest new data sources (via platforms like Estuary) and transform them into analytical readiness. Promote Best Practices: Write clean, modular, and performance-tuned SQL, treating data pipelines with a software engineering mindset (version control, code reviews, and automated deployment). What you Bringg 4+ years of experience in data analytics, BI development, or data engineering, with strong proficiency in SQL and a proven track record building or maintaining data pipelines. 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) - we use these seriously, not experimentally Comfortable owning your work independently and making technical decisions without a defined playbook Solid grasp of engineering best practices, including query optimization, version control (Git), code reviews, and thorough documentation. Good to have: Familiarity with NoSQL or operational databases (Postgres, Kafka) Experience with BI ecosystem integration (Looker, Tableau, dbt Semantic APIs) Exposure to MLOps, AI/ML pipelines, or GenAI tooling (Vertex AI, LangGraph) Experience with Infrastructure as Code (Pulumi or Terraform).