Core Skills: Spatial/GIS with ARCGIS, QGIS, PostGreSQL, PostGIS with Masters in GIS with specialization for each Job Type.
ROLE 1:
Spatial Data Engineer (ETL + Quality Focus)
Role Summary: Design, enhance, and operate scalable spatial ETL pipelines ensuring high-quality geospatial data ingestion, validation, and transformation.
Key Responsibilities
- Build and optimize ETL pipelines for spatial datasets (vector, raster, LiDAR metadata)
- Integrate new data sources into existing pipelines (APIs, files, DBs)
- Develop data validation frameworks (schema, topology, geometry checks)
- Perform RCA for data quality issues and pipeline failures
- Automate ingestion workflows using FME / Python / SQL
- Optimize pipeline performance (latency, cost, scalability)
- Implement data quality KPIs and monitoring dashboards
Tech Stack
- GIS: QGIS / ArcGIS
- DB: PostgreSQL + PostGIS
- ETL: FME, Python
- Query: SQL, Trino (nice to have)
ROLE 2:
GIS Data Analyst / Investigation Analyst (Capture Team)
Role Summary: Analyze, investigate, and validate map data issues (HVSLI) and support data capture workflows with high attention to detail.
Key Responsibilities
- Perform HVSLI investigations (map discrepancies, missing attributes)
- Analyze spatial data using ArcGIS + SQL queries
- Document findings and maintain QC reports and audit trails
- Support data upload workflows and corrections
- Collaborate with engineering teams to resolve data issues
- Validate incoming datasets against defined standards
Tech Stack
- GIS: ArcGIS
- Query: SQL
- Scripting: Python (basic)
- Optional: APIs, AWS
ROLE 3:
GIS QA/QC Engineer (SLI Validation Specialist)
Role Summary: Ensure end-to-end quality validation of map features (FC5, SLI) across markets through systematic testing and benchmarking.
Key Responsibilities
- Perform SLI validation for map features (lanes, roads, attributes)
- Execute test cases for FC5 and new attribute rollouts
- Benchmark datasets across geographies and releases
- Develop and execute QC routines and validation scripts
- Support OEM programs with quality certification
- Identify defects and drive RCA + defect closure
Tech Stack
- GIS: ArcGIS, QGIS
- Scripting: Python
- ETL/QC: FME
- DB: SQL
ROLE 4:
Senior Spatial Data Engineer (Platform + Automation + AI)
Role Summary: Design and build next-gen automated spatial data pipelines with strong focus on scalability, observability, and AI-driven enhancements.
Key Responsibilities
- Architect end-to-end data pipelines (batch + near real-time)
- Integrate multi-source data (PostGIS, Oracle, APIs, geodatabases)
- Implement data validation, monitoring, and observability frameworks
- Build reusable components and data engineering best practices
- Drive CI/CD, code quality, and version control (Git)
- Automate bulk updates, transformations, and conversions
- Leverage AI/ML (VLMs) for pre-validation / enrichment
- Own pipeline performance, reliability, and cost optimization
Tech Stack
- GIS: QGIS
- DB: PostGIS, Oracle
- Languages: Python, SQL
- Tools: FME, Git, CI/CD
- AI: VLM exposure (nice to have)
