Job Title: Data Analytics Engineer
Location: Temple Terrace, FL; Irving, TX; Basking Ridge, NJ; or Ashburn, VA
Duration: 12 months+
Working Model: Hybrid- Nothing fixed. Just the ability to go to the office two or three days a week
JOB DESCRIPTION:
Description:
We are seeking a highly skilled and results-driven Data Scientist / Analytics Engineer to join our Data Science Development team within Network Planning. In this role, you will be responsible for the core modernization, evolution, and scaling of our geospatial and statistical fiber modeling platforms—specifically our proprietary automated routing, network optimization, and Feeder Distribution Hub (FDH) modeling engines.
You will be responsible for architecting and unifying complex network routing engines, scaling high-throughput geospatial datasets, optimizing cloud database performance, and delivering high-impact spatial analytics that directly guide our national broadband infrastructure strategy.
Key Responsibilities & Essential Functions
Design, build, and optimize spatial routing and network planning models across our core automated planning platforms and fiber mapping engines.
Drive the strategic of software convergence legacy spatial planning scripts into a unified, high-performance network modeling stack.
Execute large-scale Feeder Distribution Hub (FDH) and Fiber-to-the-Home (FTTH) remodeling using Djikstra, Kruskal or other minium tree spanning algorithms utilizing network topology, and spatial datasets.
Transition and standardize spatial graph processing and routing engines
Ability to handle large datasets in spatial data formats (e.g., shapefiles, GeoJSON)
Integrate advanced spatial enterprise address databases, and master location repositories) into automated ETL pipelines for high- accuracy routing and spatial analysis.
Oversee production relational and columnar databases across cloud environments.
Lead continuous database optimization initiatives— automating table maintenance, rebuilding high-traffic spatial reference cross-reference tables, and tuning long-running queries to maintain low latency.
Build and manage automated workflow pipelines (e.g. Airflow/Python) to streamline real-time data sharing across cross-functional engineering, analytics, and AI teams.
Implement self-healing scripts, system telemetry monitoring, and strict role-based access security across the modeling stack.
Architect secure, scalable, and cost-effective data infrastructure environments across multi-cloud environments (AWS and Google Cloud Platform).
Minimum Qualifications:
Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Geographic Information Systems (GIS), Operations Research, Software Engineering, or a related quantitative field.
Experience: 5+ years of experience in data engineering, geospatial analytics, or data science roles, with demonstrated leadership in technical delivery.
Core Languages & Web Frameworks: Proficiency in Python (3.x), SQL, and microservices/web execution frameworks (Flask, FastAPI, or Django) a must.
Spatial & GIS Mastery: Deep expertise with spatial analytics tools, spatial SQL, PostGIS, ESRI/ArcGIS frameworks, and open-source mapping platforms (e.g., Overture Maps, QGIS, PG Tile Server).
Database Systems: Demonstrated experience with transactional and analytics databases (PostgreSQL, Oracle, Redshift, BigQuery)
Workflow Automation & Cloud: Hands-on experience with Apache Airflow, Docker, AWS (EC2/RDS), and Google Cloud Platform (BigQuery, Cloud Storage).
Preferred Qualifications:
Direct experience in telecommunications network planning, fiber infrastructure deployment (FTTH, FWA), or complex graph/network routing algorithms.
Proven track record of system performance tuning, query optimization, and cost-reduction initiatives across enterprise cloud environments
Key Competencies
Technical Ownership: Strong ownership mindset with a track record of driving complex engineering projects from inception to production deployment.
Collaborative Leadership: Ability to work cross-functionally across Network Planning, IT, Network Operations, and Executive Analytics teams.
Problem Solving: Exceptional analytical skills to diagnose performance bottlenecks in large-scale spatial modeling environments
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