Analytics Engineer

Arbitrip is a B2B travel-tech company building the tools that power corporate travel at scale. We're building our analytics infrastructure from scratch — and this is the hire that owns it. You'll be the first full-time data hire, working closely with the data analyst to design, build, and maintain a modern, reliable data stack.



We're looking for an Analytics Engineer to own our data infrastructure end-to-end — from ingesting raw data out of multiple sources, through building a clean, well-tested transformation layer, to delivering reliable data to analysts and business stakeholders. A core part of the role is designing and implementing our BI events schema: a unified event model that captures what happens across reservations, payments, and user activity, and makes it queryable for funnel, lifecycle, and revenue analysis. You'll be the first dedicated data hire, working closely with the data analyst and the backend team, with real ownership over technical decisions and architecture.

Arbitrip is a B2B travel-tech company building the tools that power corporate travel at scale. We're building our analytics infrastructure from scratch — and this is the hire that owns it. You'll be the first full-time data hire, working closely with the data analyst to design, build, and maintain a modern, reliable data stack.

THE ROLE

• Own the end-to-end data pipeline: ingestion from multiple sources, transformation, data quality, and delivery to analysts and BI tools.
• Design and build a unified BI events layer — a consistent event schema covering reservations, payments, search, and user activity — working from a spec defined jointly with the data analyst.
• Build and maintain dbt models across staging, transformation, and mart layers, with a focus on analytics use cases: funnel analysis, revenue tracking, and lifecycle metrics.
• Write custom Python loaders for data sources without native connectors, and configure managed connectors where they exist.
• Set up and maintain ETL orchestration: scheduling, monitoring, alerting, and failure handling across all pipelines.
• Maintain data quality through automated tests, freshness checks, and documentation — keeping the stack trustworthy as it grows.
• Collaborate with the backend team on event emission standards and with the data analyst on metric definitions and semantic layer requirements.
  
  

• BI events schema design — you have hands-on experience designing and implementing a structured business events layer: defining event taxonomies, standardizing properties across domains (e.g. payments, user actions, lifecycle transitions), and making that data reliably queryable for funnel and metrics analysis. This is a core part of the role, not a nice-to-have.
• dbt — you've built and maintained a dbt project in production. You understand model layering, incremental strategies, testing, and documentation. You write tests alongside models, not as an afterthought.
• BigQuery — strong SQL, comfortable with partitioning, clustering, and incremental loading patterns. You write queries with performance and cost in mind.
• MongoDB — you've worked with MongoDB as a data source. You understand document structure, nested fields, and how to extract and flatten data reliably for analytics.
• Python for data engineering — you've written production API-based data loaders: authentication, pagination, rate limiting, error handling, and idempotent loading into a warehouse.
• ETL orchestration — you've set up and maintained pipeline scheduling in production. You know how to handle failures gracefully, alert on them, and keep pipelines observable.
• Analytics orientation — you design data models with query patterns in mind, not just ingestion convenience. You think about how analysts and stakeholders will use what you build.
  
  

• Experience with managed ingestion tools (Fivetran, Airbyte, or similar)
• Familiarity with event analytics platforms and ingesting historical event data into a warehouse
• Experience with a semantic or metrics layer on top of a data warehouse
• CI/CD for data pipelines — automated model testing on pull requests
• Background in travel-tech, fintech, or a domain with complex transaction and lifecycle data
• Experience with CRM or support platforms as data sources
  
  

• Core stack: BigQuery
• dbt
• Python | Managed connectors + custom loaders
• Source DB: MongoDB — primary application database
• Team model: High ownership, low bureaucracy — you own the stack; analyst owns analytics
• Starting point: Build from the ground up — real decisions, not ticket-taking
• What this role is not: a support function that runs queries for other people. You'll own infrastructure decisions, maintain standards, write documentation, and be the person the analyst relies on to make data trustworthy. The data analyst owns business definitions and stakeholder reporting — you own everything upstream.