Senior Analytics / Data Engineer

We're looking for a Senior Analytics / Data Engineer to join our Data team and take ownership of Filigran's Enterprise Data Platform (EDP) end-to-end — from ingestion to consumption. This is a senior, hands-on role where you transform raw data into reliable pipelines, trusted models, and governed, high-quality data that every team can rely on for decision-making.

Core Responsibilities

The core of the role is Analytics Engineering — modeling, data quality, observability, and governance. You'll also maintain and troubleshoot ingestion pipelines when they break, and a Data Analyst / BI mindset is a valuable bonus to help teams turn data into KPIs and dashboards.

You'll collaborate with virtually every team at Filigran (Sales, Finance, Product, Engineering, Support, People) in a fully remote, high-trust, async-first environment. This is a highly cross-functional role, and you'll help the whole company become more data-driven.

Analytics Engineering — the Core

• Model and transform data with dbt, following a clean staging → intermediate → marts architecture.
• Own data quality, reliability, and observability: testing, monitoring, alerting on data issues, and code reviews.
• Maintain the data catalog and governance so data stays discoverable, understood, trusted, and well-documented.
• Build and maintain dashboards in Metabase that give teams clear, trustworthy visibility into their KPIs.

Data Engineering — Necessary Skills

• Maintain and scale ingestion pipelines (Airbyte / Airflow) that bring data from external APIs and sources into the warehouse.
• Investigate and fix pipelines when they break, ensuring the platform keeps running reliably end-to-end.

Data Analyst / BI — Valuable Bonus

• Partner with teams across the company to define metrics, model their KPIs, and support decision-making.
• Help teams build self-serve analytics and dashboards on top of the platform.

Collaboration

• Collaborate with stakeholders across every team (Sales, Finance, Product, Engineering, Support, People) to turn their needs into data products.
• Work closely with the Engineering team on deployment, monitoring, and platform reliability.

Technical Profile

• 5+ years of experience in Analytics Engineering / Data Engineering, with strong hands-on expertise in dbt: modeling, testing, data quality, observability, and governance in production.
• Experience with ingestion and orchestration pipelines (Airflow, Airbyte) — enough to maintain, debug, and fix them when they break.
• Proficient in SQL and Python for data transformation, ingestion, and scripting.
• Cross-functional mindset: enjoy talking to stakeholders, understanding their problems, and translating them into KPIs and dashboards.
• Strong autonomy and end-to-end ownership: take a scoped project across the whole stack (ingestion → transformation → consumption) and drive it independently in an async-first environment.
• Solid grasp of software engineering best practices: versioning, code reviews, testing, CI/CD.
• Product-oriented mindset: care about data quality, usability, and real business outcomes.

Bonus if you have:

• Experience building AI agents to make data and platforms more accessible (e.g., natural-language querying, agentic assistants over the data platform).
• Familiarity with LLM/agent stacks (tool calling, MCP integrations).
• Experience working fully remote in an async-first culture.
• Experience in the cybersecurity industry.

About the Data Platform

Filigran's data platform powers decision-making across the entire company. It ingests data from dozens of sources (Airbyte / Airflow), transforms it into trusted models with dbt, and serves it through Metabase dashboards and a maintained data catalog.

We have ambitious plans to take the platform to the next level, and you'll help shape them:

• AI agents to make data more accessible — natural-language querying and agentic assistants so anyone at Filigran can get answers without writing SQL.
• A self-reporting agent that automatically generates and distributes insights and narratives from the data.
• A proactive alerting system that watches key metrics and notifies teams when values cross meaningful thresholds.
• A semantic layer (e.g., Cube) to define metrics once and expose them consistently across every tool and consumer.