Data Engineer

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.