Senior Analytics / Data Engineer - Enterprise Data Platform

Role overview

A senior, hands-on analytics and data engineering role owning an enterprise data platform end-to-end, from ingestion through transformation to consumption. The position is primarily analytics engineering, modelling and governing data with dbt, while also requiring enough data engineering skill to maintain and debug ingestion pipelines. Working in a fully remote, async-first environment, the role partners with teams across sales, finance, product, engineering, support, and people to turn raw data into trusted, decision-ready assets.

Responsibilities

- Model and transform data with dbt, following a clean staging, intermediate, and marts architecture. - Own data quality, reliability, and observability, including testing, monitoring, alerting, and code review. - Maintain the data catalog and governance so data stays discoverable, well-documented, and trusted. - Build and maintain dashboards in Metabase that give teams clear, trustworthy visibility into their KPIs. - Maintain and scale ingestion pipelines built on Airbyte and Airflow, bringing data from external APIs into the warehouse. - Investigate and fix broken pipelines, ensuring the platform keeps running reliably end-to-end. - Partner with teams across the company to define metrics, model their KPIs, and support self-serve analytics. - Conduct technical watch, continuously improving the platform's architecture, tooling, and ways of working.

Requirements

- Five or more years of experience in analytics engineering or data engineering, with strong hands-on expertise in dbt for modelling, testing, data quality, observability, and governance in production. - Experience with ingestion and orchestration pipelines using Airflow, Airbyte, or equivalent tools, enough to maintain and debug them. - Proficiency in SQL and Python for data transformation, ingestion, and scripting. - A cross-functional mindset, comfortable translating stakeholder problems into KPIs and dashboards. - Strong autonomy and end-to-end ownership, able to drive scoped projects independently in an async-first environment. - Solid grasp of software engineering best practices, including versioning, code reviews, testing, and CI/CD. - A product-oriented approach with a focus on data quality, usability, and real business outcomes.

Nice to have

- Experience building AI agents to make data more accessible, such as natural-language querying or agentic assistants over the data platform. - Familiarity with LLM and agent stacks, including tool calling and MCP integrations. - Background working fully remote within an async-first culture. - Experience in the cybersecurity industry.

Benefits and work setup

- Fully remote, flexible, and balanced working designed to fit around life. - Competitive pay plus equity so everyone shares in company success. - Choice of gear and a setup budget tailored to the individual. - Twice-yearly in-person regional and global offsites to connect and strengthen culture.