Spatial Data Engineer — Ingestion & Conflation, Equity

Augmodo; California (MO); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

401k

Job summary

Augmodo is seeking a Software Engineer to own the Spatial Data Ingestion & Conflation Engine. You will build pipelines that turn edge data into high-fidelity, production-ready insights for retailers and brands.

You will implement robust rules-based normalization and strategically integrate LLMs to solve complex data matching and entity resolution, while keeping costs in check and ensuring versioned data evolution across pipelines.

Qualifications

• 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 prefer simple heuristics when appropriate.
• Engineering rigor with versioning for code and data schemas.

Responsibilities

• Build Staged Pipelines: design and maintain versioned data pipelines for ingestion and conflation of complex data.
• High-Fidelity Output: ensure data outputs meet high accuracy and reliability for dashboards.
• Hybrid AI Strategy: use rules-based normalization plus selective LLM integration for complex matches.
• Scale & Cost Management: design scalable, cost-effective, performant solutions for retail data scales.
• Versioned Data Evolution: manage data schema and pipeline lifecycles for rapid iteration without breaking consumers.

Skills

Data pipelines

Entity resolution

LLMs

Regex / heuristics

Version control

Tools

Python

SQL

ETL pipelines

Job description

Augmodo is seeking a Software Engineer to own the Spatial Data Ingestion & Conflation Engine. You will build pipelines that turn edge data into high-fidelity, production-ready insights for retailers and brands.

You will implement robust rules-based normalization and strategically integrate LLMs to solve complex data matching and entity resolution, while keeping costs in check and ensuring versioned data evolution across pipelines.