Full-Stack Data Engineer
Summary
Builds the data backbone for an AI-driven public-safety platform, integrating legacy systems, cloud services, and real customer data with ETL pipelines and infrastructure-as-code.
We\'re building AI for public safety. It only works if the underlying data is connected, and that\'s the job.
Focus of the role:
We\'re looking for a full-stack data engineer to build the data backbone that connects our product, back-end services and cloud infrastructure to real customer data. You\'d be focused on data modelling, pipeline construction, and last-mile integrations and connectors. This suits someone who moves fast, owns work end to end, and is comfortable both in ambiguity and in messy legacy environments.
Qualifications:
Must-have — production experience building and operating back-end services with infrastructure-as-code; data modelling across legacy systems and back-end-to-warehouse flows; ETL, schema mapping and reconciliation across legacy and modern systems; solid AWS/cloud with the ability to deliver end-to-end. Bonus — regulated data (sensitive PII, audit trails, access control); joining data across multiple source systems; zero-copy/virtualisation-first patterns or data-platform-vendor experience (Snowflake, Databricks, Datadog); building ingestion for legacy or closed systems where clean APIs don\'t exist; dual cloud + on-prem deployment; CE/CE+ or security-conscious development in government/law-enforcement contexts.
Tech stack:
Node / NestJS / TypeScript / Drizzle / Python; PostgreSQL, Redis/Valkey, vector DBs; AWS (VPC, ECS, Lambda, S3, KMS, GuardDuty/SecurityHub); a warehouse (Redshift, Glue, Snowflake or similar); orchestration (Airflow, dbt or Prefect); streaming/connectivity (Kafka, JDBC/ODBC); Pulumi or Terraform.
If you\'re excited by this but not sure you tick every box, reach out anyway. We\'d rather hear from you than have you count yourself out.