Global Head of Data Engineering
Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Nov 2, 2026.
About Kape Technologies
Kape is a cybersecurity company focused on helping everyone have a better digital experience with greater privacy and protection. With over 1,000 experienced individuals across ten global locations, the Kape team is guided by a single mission: to create a more secure and safe world.
The Role
If you want to lead data engineering for a global, AI-first company, this is a rare opportunity. Kape is behind ExpressVPN and other privacy brands, and our data platform is at the core of how the company works, serving analysts, engineering teams, leadership and a growing set of AI tools.
As Global Head of Data Engineering, you'll start hands-on, then build and lead a team across several countries, with a real say in where the platform goes next. You will:
Write SQL, Python and pipelines yourself in your first months
Lead, coach and grow a team of data engineers across several countries
Set team priorities and plan delivery, and own hiring and onboarding
Own the platform's architecture for ingestion, anonymization and enrichment of data, and decide what we keep, change or replace
Set up a single source of truth: shared metric definitions, building on our semantic layer, that dashboards and AI tools both use
Set the engineering standards: testing, code review, CI/CD, documentation and alerting
Partner with finance, marketing, product and engineering to turn business needs into data solutions
Our stack today: Python, SQL, Snowflake, Redshift, BigQuery, Airflow, dbt, AWS, Kafka, Terraform, Tableau and Streamlit. You decide what stays.
What We're Looking For
8+ years in data engineering or analytics engineering, including 3+ years leading a team
You have led a data platform migration or rebuild end to end, with live dashboards and users depending on it
Strong hands-on SQL and Python, and you still write code
Deep experience with data modeling, reliable pipelines (Airflow, dbt or similar) and cloud warehouses, ideally on AWS
You use AI tools daily and know where not to trust them
Clear communication with engineers and non-technical leaders alike
Bonus: semantic layers or data contracts, subscription-business data (renewals, churn, LTV), Terraform, and data privacy in production systems.