Director of Data & Analytics
Hack The Box
| Company | Hack The Box |
| Category | Data & Analytics |
| Location | London |
| Remote | On-site (inferred) |
| Employment | Full-time |
| Level | Director |
| Salary | Not stated by the employer |
| Posted | 18 Jun 2026 |
| Last verified | 9 Aug 2026 |
| Source | Employer ATS (workable) |
Description
✨The core mission of the Director of Data & Analytics: Hack The Box is on a mission to redefine cybersecurity readiness for the AI era — and data is the engine room of that mission. As Director of Data & Analytics, you will own the execution of HTB's data platform and analytics strategy: a GCP-native ecosystem (BigQuery, Pub/Sub, Dataflow, Bigtable but also some legacy in Aiven - kafka / Clickhouse - Snowflake) serving analytics, ML, and AI workloads across every product line, from Academy and Labs to Enterprise and AI Range. In your first 6 months, you'll take operational command of a 10–12 person team across data engineering, analytics engineering, and BI, drive our Snowflake-to-BigQuery consolidation to the finish line, and establish delivery rhythms, SLOs, and quality gates that make the platform boringly reliable. Over the next year, you'll scale the function: company-wide data governance and data contracts, self-serve analytics adopted by every department, and a data platform that powers HTB's AI products — not just its dashboards. 🏢 Location & Work Mode: Athens, Attica, Greece Hybrid (2 days in the office, 3 days remote). Those based within 55 km of our Athens office will follow a hybrid work model. For candidates located beyond that radius, a fully remote arrangement is available. ✈️ Relocation We offer a 10% relocation bonus on the annual salary to support your move. In addition, you would benefit from a 50% tax reduction on your income as part of the relocation. We'll support you with accommodation during the first few weeks 🍺 The fellowship you’ll be joining: You'll lead a guild of 10–12 data professionals spanning three crafts: Data Engineers building and operating streaming and batch pipelines (Pub/Sub, Dataflow, Kafka, Airflow) and the BigQuery/Bigtable platform Analytics Engineers owning the layered modeling of our warehouse and the semantic foundations of company metrics BI & Analytics specialists turning platform data into decisions for Product, Growth, Finance, and the executive team You will report directly to the VP of Data, Analytics & AI, and work side by side with the AI/ML infrastructure function (self-hosted GPU inference, LLM platform) within the same department. Your key stakeholders outside the department: Software Development, Infrastructure Product Management, Finance, Growth/Marketing, and the executive team, who rely on your team's numbers to steer the company. ⚔️ Technology tools & weapons you’ll be using: Google Cloud Platform: BigQuery, Pub/Sub, Dataflow (Apache Beam), Bigtable, Vertex AI Feature Store Apache Flink/Spark Streaming & messaging: Kafka (Aiven), Pub/Sub with Avro schemas and schema registry (Karapace) Orchestration & transformation: Airflow, layered BigQuery modeling, Python Analytics & BI: modern BI tooling(Looker/Tableau), product analytics (Amplitude), experimentation platforms. Governance: data contracts, CI/CD quality gates 📚 Interesting resources you should check: Humans of HTB #8: Asterios's journey into Data Engineering 🚀 The adventures that await you after becoming Director of Data & Analytics at Hack The Box: Lead and grow a 10 person team of data engineers, analytics engineers, and BI specialists — hiring, coaching, and developing the next generation of leads within the team Own the delivery roadmap of the data platform: prioritize across stakeholder demands, set quarterly OKRs, and keep commitments visible and honest Drive the consolidation onto our GCP-native architecture (BigQuery, Pub/Sub, Dataflow, Bigtable), completing the migration off legacy warehouses and CDC pipelines Operationalize data governance company-wide: schema contracts, compatibility tiers, CI/CD gates, and documented business semantics across services Establish engineering discipline: SLOs for data freshness and pipeline reliability, cost observability (