Spatial Data Engineer: Ingestion & Conflation Engine

Augmodo; United States; Remote; USD 150,000 - 200,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401k

Job summary

Augmodo is hiring a Software Engineer to own the Spatial Data Ingestion & Conflation Engine. You will build pipelines that transform raw edge data into high-fidelity, production-ready insights for store-level applications.

The role emphasizes precision at scale, versioned data schemas, and cost-efficient AI integrations. You will blend robust rules-based normalization with LLMs to tackle complex data matching while ensuring outputs suit consumer dashboards.

Qualifications

• Extensive experience building reliable, staged pipelines for complex ingestion tasks.
• Experience merging disparate, messy data sources into a unified “Golden Record.”
• Comfort with LLMs and prompt engineering but know when a simple regex or bounded heuristic is better.
• Engineering rigor: versioning everything—from code to data schemas.

Responsibilities

• Build Staged Pipelines: Design and maintain versioned data pipelines that handle ingestion and conflation of complex retail data.
• High-Fidelity Output: Ensure outputs meet a high quality bar for accuracy and reliability.
• Hybrid AI Strategy: Use rules-based normalization and RegEx while integrating LLMs for complex data matching.
• Scale & Cost Management: Architect cost-efficient, performant solutions for large retail environments.
• Versioned Data Evolution: Manage data schema and pipeline lifecycle for rapid iteration without breaking downstream insights.

Skills

Data ingestion pipelines

Entity resolution

Regex/heuristics

Versioning/code/data schemas

Tools

LLMs

Prompt engineering

Job description

Augmodo is hiring a Software Engineer to own the Spatial Data Ingestion & Conflation Engine. You will build pipelines that transform raw edge data into high-fidelity, production-ready insights for store-level applications.

The role emphasizes precision at scale, versioned data schemas, and cost-efficient AI integrations. You will blend robust rules-based normalization with LLMs to tackle complex data matching while ensuring outputs suit consumer dashboards.