Senior Data Engineer (Data Ops, Search and Matching)

Gensoft is hiring a hands-on Senior Data Engineer to join an AI-powered search and matching platform. Your focus will be the data pipelines that feed production search: making ingestion fast, reliable and easy to operate, and keeping the data that reaches the matching layer clean and consistent.

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

Nice to have
Direct experience with pgvector and Amazon Bedrock
Experience integrating and evaluating embedding models (e.g. VoyageAI)
Python for data processing
Entity resolution and search relevance
Background in HR tech or recruitment data

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