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We develop GeoMatch as a novel, scalable, and efficient big-data pipeline for large-scale map matching on Apache Spark. GeoMatch improves existing spatial big-data solutions by utilizing a novel spatial partitioning scheme inspired by Hilbert space-filling curves. Thanks to its partitioning scheme, GeoMatch can effectively balance operations across different processing units and achieve significant performance gains. GeoMatch also incorporates a dynamically adjustable error-correction technique that provides robustness against positioning errors. We demonstrate the effectiveness of GeoMatch through rigorous and extensive empirical benchmarks that consider large-scale urban spatial datasets ranging from 166,253 to 3.78B location measurements. We separately assess execution performance and accuracy of map matching and develop a benchmark framework for evaluating large-scale map matching. Results of our evaluation show up to 27.25-fold performance improvements compared to previous works while achieving better processing accuracy than current solutions. We also showcase the practical potential of GeoMatch with two urban management applications. GeoMatch and our benchmark framework are open-source.