Senior Software Engineer - Analytics

Role Overview: As a Senior Software Engineer - Analytics at LogiNext, you will be responsible for building data products that extract valuable business insights for efficiency and customer experience. Your role will involve managing, processing, and analyzing large amounts of raw information in scalable databases. Additionally, you will be developing unique data structures and writing algorithms for new products. Critical thinking and problem-solving skills will be essential, along with experience in software development and advanced algorithms. Exposure to statistics and machine learning algorithms as well as familiarity with cloud environments, continuous integration, and agile scrum processes will be beneficial. Key Responsibilities: - Develop software that generates data-driven intelligence in products dealing with Big Data backends - Conduct exploratory analysis of data to design efficient data structures and algorithms - Manage data in large-scale data stores (e.g., NoSQL DBs, time series DBs, Geospatial DBs) - Create metrics and evaluate algorithms for improved accuracy and recall - Ensure efficient data access and usage through methods like indexing and clustering - Collaborate with engineering and product development teams Qualifications Required: - Master's or Bachelor's degree in Engineering (Computer Science, Information Technology, Information Systems, or related field) from a top-tier school, or a master's degree or higher in Statistics, Mathematics, with a background in software development - 4 to 7 years of experience in product development with algorithmic work - 3+ years of experience working with large data sets or conducting large-scale quantitative analysis - Understanding of SaaS-based products and services - Strong algorithmic problem-solving skills - Ability to mentor and manage a team, taking responsibility for team deadlines - Proficiency in Python programming language - Experience with data processing analytics and visualization tools in Python (e.g., pandas, matplotlib, Scipy) - Strong understanding of SQL and querying NoSQL databases (e.g., Mongo, Cassandra, Redis) - Understanding of working with and managing large databases, such as indexing, sharding, caching, etc. - Exposure to Big Data technologies like Hadoop, Yarn, Redshift, Spark, Kafka, Pig/Hive - Exposure to machine learning algorithms - Familiarity with geospatial data stores, with exposure to PostGIS being a plus - Desirable exposure to data visualization tools,