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Posted Apr 12, 2026

US – Data Engineer (Pipelines & Structured Markup), Part Time Role

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This is a remote position. US – Data Engineer (Pipelines & Structured Markup), Part Time Title: Data Engineer – Pipelines & Structured Markup Location: US (Part Time, Remote or Hybrid) Company: Vulcury LLC Role Overview Vulcury is building a manufacturing intelligence infrastructure that converts raw interactions — interviews, transcripts, CAD uploads, commercial discussions — into structured, queriable data objects. We are seeking a Part Time Data Engineer to design and maintain ingestion pipelines and structured transformation workflows that power our internal semantic “truth layer.” This is not a reporting role. This is a semantic infrastructure role. Responsibilities • Build and maintain ingestion pipelines (Python-based ETL/ELT) • Design structured transformation workflows using dbt, SQLMesh, or equivalent • Convert unstructured transcripts and documents into normalized database records • Maintain PostgreSQL architecture (structured tables, JSONB, indexing strategy) • Develop attribute extraction frameworks for technical, commercial, and risk signals • Ensure data quality, consistency, and lineage from raw interaction to structured output • Collaborate with AI/ML engineers to ensure clean model inputs Requirements Required Skills • Strong Python (data pipelines, orchestration) • Advanced SQL (PostgreSQL preferred) • Experience with ETL/ELT frameworks (dbt, Airflow, SQLMesh, etc.) • Experience handling semi-structured data (JSON, transcripts, document parsing) • Strong schema design and normalization skills • Familiarity with cloud storage systems (S3 or equivalent) Nice to Have • Experience building semantic layers or knowledge graphs • Experience working with manufacturing or technical data • Familiarity with vector databases Benefits What Success Looks Like • Raw interviews automatically convert into structured records • Attribute confidence scoring flows downstream cleanly • Data lineage is fully traceable • Query performance remains stable as data volume scales
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