Job Details

Senior Data Engineer (Big Data)

Advertiser
StyleSeat
Location
San Francisco, California, United States
Rate
-
Senior Data Engineer (Big Data) San Francisco, CA or 100 Remote A Little Bit About StyleSeat As a Senior Data Engineer (Big Data) 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. In Your New Role As a Senior Data Engineer (Big Data) 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 (Big Data) 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. Our 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. What yoursquod be doing Working on StyleSeat's Data Infrastructure Projects Demonstrating an interest in Big Data Technologies Developing and further developing Big Data processing pipelines for data sources containing structured and unstructured data Monitoring and optimizing key infrastructure components such as Databases, EC2 Clusters, Containers and other aspects of the stack Helping promote best practices for Big Data development at StyleSeat Acting as a bridge between the Data Engineering team and the wider Engineering organization Working closely with our senior Data Analysts Working with the Data Science team on crossover initiatives Working 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 Analyzing requirements and architecture specifications to create detailed design Researching areas of interest to the team and help facilitate solutions About you Yoursquove had experience at a bigger startup where yoursquove worked with big data architecture, and you were there while they scaled and can bring that experience to help us scale You have a can-do attitude and you see your cross-functional work as equally important as the work within your immediate team Yoursquore not afraid to challenge the status quo and suggest alternate architecture, and you actively encourage others to do so While you own everything you do, you also keep the bigger picture in mind Requirements 7+ years as a Backend Software Engineer or as a Data Engineer (Python Required) 2+ years of experience with AWS Data Infrastructure, including RDS, RedShift S3 2+ years building data pipelines in a high ingestion environment with varied forms of data infrastructure technologies Experience designing, developing, and owning ETL pipelines that deliver data with measurable quality under a pre-defined SLA Proficiency with Python, SQL and other scripting languages Experience using SQL daily to scale and optimize schemas, and performance-tune ETL pipelines An ability to identify and resolve pipeline issues, and discover opportunities for improvement in complex designs or coding schemes Experience monitoring existing metrics, analyzing data, and partnering with other internal teams to solve difficult problems creating a better customer experience Nice-to-haves Experience with data streaming technologies e.g. Spark, Storm, Flink Experience with message queue systems e.g. Kafka, Kinesis Experience with any of the following message file formats Parquet, Avro, Protocol Buffer Experience with Redis, Cassandra, MongoDB or similar NoSQL databases

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