With the rapid growth of genomic data, the need for scalable data mining algorithms has increased. Frequent contiguous sequence mining is a technique that can help biologists to better understand the function and structure of our DNA, by capturing the common characteristics among related sequences. Many sequence mining algorithms have been developed over time. However, most of them suffer from scaling issues when dealing with big data or give no warranty for the completeness of their result. In this paper, we propose a distributed sequential pattern mining algorithm implemented on Apache Spark. Specifically, the algorithm exploits the Apriori Property and information about each patterns location within the original sequence, to drastically reduce the number of candidates at each iteration. Experimental results on real-world datasets confirm our performance expectations, showing a better scalability when compared to other distributed solutions.