The Sr. Quality Engineer will advance the maturity of quality engineering practices by applying spatial data engineering techniques to validate data quality across extraction, transformation, and loading stages using automated geospatial workflows. 

Key responsibilities include supporting and extending workflows for testing and validation purposes. Partner with Data Science teams for the development of geospatial machine‑learning models for advanced quality evaluation in accordance with SD/HD Map specifications. 

• Technical Subject Matter Expert (SME) for spatial data models and automated geospatial quality-testing methodologies adhering to SD/HD map specifications.

• Plan, schedule, and oversee automated data annotation, spatial integrity checks, and validation tasks aligned with Product Testing frameworks and evolving testing requirements.  

• Implement, and optimize automated QA pipelines using geospatial processing frameworks.  

• Integrating machine learning–powered validation signals by collaborating with Data Science to integrate outputs from spatial ML models for anomaly detection, classification, feature validation, and predictive quality scoring.  

• Integrate and validate API driven ingestion processes within the quality framework including JSON/XML parsing, schema validation, and automated integration with internal and external data sources. 

• Lead continuous improvement efforts of system inefficiencies via root cause analyses, and engineering enhancement to improve automation efficiency in testing workflows.  

• Develop, document, and enforce technical testing procedures, ensuring consistency, repeatability, and compliance across global teams and distributed test environments.  

• Provide technical training and mentorship to internal and external contributors on advanced scripting, automation tools, geospatial processing techniques, and cloud based validation workflows.   

• Bachelor’s degree required; GIS/Geography, Computer Science, Engineering, Data Science, or related fields preferred. 

• 7+ years of experience in geospatial quality testing, spatial data engineering, or related technical roles. 

• Strong understanding of SD/HD map databases, geospatial schemas, and location-based data products. 

Machine Learning & AI -Experience applying geospatial machine learning and AI models for classification, anomaly detection, clustering, or automated quality checks is a strong plus. 

Cloud & Architecture -Experience with AWS services, including architecture design, data pipelines, monitoring, and application deployment is advantageous. 

Advanced Scripting & Data Engineering 

• Advanced proficiency in Python, including automation scripting, spatial libraries, and data engineering workflows. 

• Strong experience with SQL, relational databases, spatial SQL, and performance optimized spatial queries. 

• Proficiency in API based data extraction, JSON/XML parsing, and integrating external data sources into automated pipelines. 

Spatial Processing & GIS Tools -Advanced experience with FME, ArcGIS, QGIS, and spatial data transformation workflows. 

Automation & Pipeline Development 

• Demonstrable experience building robust, automated spatial data pipelines, including QA/QC, feature extraction, integrity checks, and batch processing. 

• Familiarity with geospatial data engineering 

Analytics & Visualization -Experience with Tableau, analytics, and building insights dashboards. Strong ability to work with cross-functional analytics teams. 

 Coupling the skills above with strong internal attribute domain knowledge, advanced analytical abilities, and deep understanding of HERE’s product ecosystem is a significant advantage.