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\nPosition: Senior Data Engineer\n\nLocation: San Francisco, CA OR 100% Remote (U.S. Based ONLY)\n\nA Little Bit About StyleSeat:\n\n\n\n\n\nSenior Data Engineer\n\n\n\n\n\nAs a Senior Data Engineer at StyleSeat, you will have a rare opportunity to join a startup empowering small business owners across the country to be more successful doing what they love. Our mission is to help people look and feel their best. We are on the path to achieving this mission by being the go-to marketplace for consumers to discover, book, and pay for beauty and grooming services (hair stylists, colorists, nail artists, estheticians, barbers, etc). We are also the premier solution for all independent professionals in the industry to run and grow their business. We have powered over 120 million appointments booked and $10B in revenue for small businesses and are on the path to much more.\n\nIn Your New Role:\n\nAs a Senior Data Engineer you will join an impactful, multi-functional team of data scientists, analysts, data engineers and backend engineers dedicated to creating a data-driven culture. A team where everyone is active in defining the product and development process. As a result, you will know where your initiative and drive can best make a difference and be recognized. You'll know the internal and external customers with whom we are working,, and the needs of each one. The Senior Data Engineer will utilize their experience and create appropriate solutions and tools to solve complex data engineering problems. StyleSeat is a rapidly scaling company making this the best environment to take on ownership as well as learn how to grow a company.\n\nOur engineering team consists of developers from a wide array of backgrounds. Our team is a tight knit, friendly group of engineers that is dedicated to learning from and teaching each other. Team members regularly contribute to and optimize our engineering best practices and processes. Our team wants to make software engineering fun, easy, and fulfilling, so we've come up with a set of values that we apply to our software every day: Flexible, Consistent, Predictable, Efficient, and Pragmatic.\n\nWhat you’ll be doing:\n\n\n* \n\nWorking on StyleSeat's Data & Infrastructure Projects\n\n\n* \n\nExperience or demonstrated interest in Big Data Technologies\n\n\n* \n\nDevelop and further develop Big Data processing pipelines for data sources containing structured\n\nand unstructured data\n\n\n* \n\nMonitor and optimize key infrastructure components such as Databases, EC2 Clusters,\n\nContainers and other aspects of the stack\n\n\n* \n\nHelp promote best practices for Big Data development at StyleSeat\n\n\n* \n\nAct as a bridge between the Data Engineering team and the wider Engineering organization\n\n\n* \n\nWorking closely with our senior Data Analysts\n\n\n* \n\nWork with the Data Science team on crossover initiatives\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* \n\nWork in an Agile manner with business users, data analysts and data scientists to understand and discover the potential business value of new and existing Data Sets and help productize those discoveries\n\n\n* \n\nAnalyzes requirements and architecture specifications to create detailed design\n\n\n* \n\nResearch areas of interest to the team and help facilitate solutions\n\nAbout you:\n\n\n\n\n\n\n* \n\nYou’ve had experience at a bigger startup where you’ve worked with big data architecture, and you were there while they scaled and can bring that experience to help us scale\n\n\n* \n\nYou have a can-do attitude and you see your cross-functional work as equally important as the work within your immediate team\n\n\n* \n\nYou’re not afraid to challenge the status quo and suggest alternate architecture, and you actively encourage others to do so\n\n\n* \n\nWhile you own everything you do, you also keep the bigger picture in mind\n\nWhat you should bring to the table:\n\n\n\n\n\n\n* \n\n7+ years as a Backend Software Engineer or as a Data Engineer\n\n\n* \n\n2+ years of experience with AWS Data Infrastructure, including RDS, RedShift & S3\n\n\n* \n\n2+ years building data pipelines in a high ingestion environment with varied forms of data\n\ninfrastructure technologies\n\n\n\n\n\n\n* \n\nDesigning, Developing, and Owning ETL pipelines that deliver data with measurable quality under\n\npre-defined SLA\n\n\n* \n\nProficiency with Python, SQL and other scripting languages\n\n\n* \n\nUsing SQL daily to scale and optimize schemas, and performance-tune ETL pipelines\n\n\n* \n\nIdentifying and resolving pipeline issues, and discovering opportunities for improvement in in\n\ncomplex designs or coding schemes\n\n\n* \n\nMonitoring existing metrics, analyzing data, and partnering with other internal teams to solve\n\ndifficult problems creating a better customer experience\n\nSome plusses:\n\n\n\n\n\n\n* \n\nExperience with data streaming technologies e.g. Spark, Storm, Flink\n\n\n* \n\nExperience with message queue systems e.g. Kafka, Kinesis\n\n\n* \n\nExperience with any of the following message / file formats: Parquet, Avro, Protocol Buffer\n\n\n* \n\nExperience with Redis, Cassandra, MongoDB or similar NoSQL databases\n\n\n* \n\nExperience with containerization frameworks and tools e.g. Docker, Terraform, Kubernetes\n\n\n* \n\nGathering requirements, scoping, architecting, developing, building, releasing, and maintaining\n\ndata oriented projects for different parts of the organization, while taking into account\n\nperformance, stability and an error-free operation\n\n\n* \n\nArchitecting scalable and reliable data solutions to move data across systems from multiple\n\nproducts in nearly real time\n\n\n\n

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