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

Remote Full-time
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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