Data Engineer

We are looking for Data Engineer/Golang. 

Details: 

Engagement: Full-time contract, 6 months to start, with a strong chance of extension

Fully remote

Monday to Friday, 8 hours a day, with overlap with the team’s core working hours

Start: as soon as possible

Tech stack

Go (orchestration), PostgreSQL with pgvector, embedding models served through Amazon Bedrock.

What you’ll do

Take over the current ingestion flow and document how it works: sources, transformations, weak points and failure scenarios

Automate ingestion, validation, deduplication and incremental updates, and turn onboarding of new data sources into a repeatable process

Build safe recovery mechanisms (retries, checkpoints, replay and backfill, quarantine) so that no data is lost or duplicated

Track record counts, metadata, source identifiers and data lineage; catch schema changes and run quality checks

Support the interfaces of the matching service and take part in improving embeddings, indexing and match quality

Set up monitoring and alerting together with DevOps (freshness, failures, throughput, latency, cost)

Keep tests, deployment practices and runbooks in shape so the system can be run and recovered without you

Apply the agreed rules on data consent, access, retention and deletion, and flag any open questions early

Work closely with Product, Engineering, DevOps and ML specialists, keep the team updated on progress

What we expect

Proven ownership of production data pipelines, including debugging and recovering failures

Strong SQL and PostgreSQL data modelling skills

Practical experience with Go for orchestration and migrations

Solid understanding of APIs, batch and async processing, schema evolution, metadata and lineage

Experience with idempotent processing, reconciliation and safe reruns

Hands-on work with vector search and embedding pipelines in production (search, matching or ML products)

Ability to trace quality problems across inputs, embeddings, indexes and downstream results

Cloud experience, automated testing, code review and disciplined deployments

Monitoring, alerting, runbooks, privacy-aware data modelling and access control

Hiring process

CV review, technical interview, live coding session, final interview.