{"id":3815504,"url":"https://alion.io/job/gensoft-senior-data-engineer-data-ops-search-and-matching","title":"Senior Data Engineer (Data Ops, Search and Matching)","company":{"id":4516475,"name":"GenSoft","domain":"gensoft.pro","url":"https://alion.io/company/gensoft-2","size_band":null,"is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ukraine"],"countries":["UA"],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"SQL","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-11-05T13:37:39Z","employer_posted_date":null,"last_verified_at":"2026-11-05T13:37:39Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"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.\n\nEngagement\n\nFull-time contract, 6 months to start, with a strong chance of extension\n\nFully remote\n\nMonday to Friday, 8 hours a day, with overlap with the team’s core working hours\n\nStart: as soon as possible\n\nTech stack\n\nGo (orchestration), PostgreSQL with pgvector, embedding models served through Amazon Bedrock.\n\nWhat you’ll do\n\nTake over the current ingestion flow and document how it works: sources, transformations, weak points and failure scenarios\n\nAutomate ingestion, validation, deduplication and incremental updates, and turn onboarding of new data sources into a repeatable process\n\nBuild safe recovery mechanisms (retries, checkpoints, replay and backfill, quarantine) so that no data is lost or duplicated\n\nTrack record counts, metadata, source identifiers and data lineage; catch schema changes and run quality checks\n\nSupport the interfaces of the matching service and take part in improving embeddings, indexing and match quality\n\nSet up monitoring and alerting together with DevOps (freshness, failures, throughput, latency, cost)\n\nKeep tests, deployment practices and runbooks in shape so the system can be run and recovered without you\n\nApply the agreed rules on data consent, access, retention and deletion, and flag any open questions early\n\nWork closely with Product, Engineering, DevOps and ML specialists, keep the team updated on progress\n\nWhat we expect\n\nProven ownership of production data pipelines, including debugging and recovering failures\n\nStrong SQL and PostgreSQL data modelling skills\n\nPractical experience with Go for orchestration and migrations\n\nSolid understanding of APIs, batch and async processing, schema evolution, metadata and lineage\n\nExperience with idempotent processing, reconciliation and safe reruns\n\nHands-on work with vector search and embedding pipelines in production (search, matching or ML products)\n\nAbility to trace quality problems across inputs, embeddings, indexes and downstream results\n\nCloud experience, automated testing, code review and disciplined deployments\n\nMonitoring, alerting, runbooks, privacy-aware data modelling and access control\n\nNice to have\n\nDirect experience with pgvector and Amazon Bedrock\n\nExperience integrating and evaluating embedding models (e.g. VoyageAI)\n\nPython for data processing\n\nEntity resolution and search relevance\n\nBackground in HR tech or recruitment data\n\nHiring process\n\nCV review, technical interview, live coding session, final 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