Stealth AI Startup (Policing)
Full-Stack Data Engineer
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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
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- 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
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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
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