Data Engineer With Demographic and Geospatial Data Experience
CLIENT TECHNOLOGY SOLUTIONS LLC
United States
5 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
Tech stack
Geographic Information Systems
Apache HTTP Server
Automation of Tests
Big Data
Code Review
Continuous Integration
Data Deduplication
Python (Programming Language)
Operational Databases
Reference Data
Standard Sql
Apache Spark
+5 more
Software Troubleshooting
Git
Data Lakes
Pyspark
Databricks
Requirements
- Strong Databricks, Python, PySpark and SQL skills, with hands-on experience developing and supporting production data pipelines processing large datasets.
- Experience with Delta Lake, batch and incremental ingestion, schema management, merge logic, deduplication and maintaining historical records without losing the meaning of effective dates.
- Practical geospatial data engineering experience working with points, polygons, spatial joins, geometry validation and geographic aggregation.
- Understanding of coordinate reference systems, including British National Grid and WGS84, coordinate transformation, distance units and the limitations of postcode centroids for precise location analysis.
- Experience with a scalable Spark spatial approach, such as supported native Databricks spatial functions or Apache Sedona. Able to select tooling compatible with the deployed runtime and workload.
- Ability to ingest and transform spatial formats such as GeoJSON, WKT/WKB and Shapefile or GeoParquet, alongside tabular reference files.
- Experience joining demographic or other record-level data to geographic reference data, handling unmatched and multiple matches, and controlling changes to lookup versions and boundaries.
- Strong data quality and reconciliation skills, including tests that identify record multiplication, missing mappings and incorrect geographic assignments.
- Experience with Git, CI/CD, code review, automated testing, job monitoring and performance troubleshooting. Able to document assumptions and explain data issues clearly to analysts and data owners
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