Senior Data Platform Engineer

100% Remote (U.S. based only)

A Little Bit About StyleSeat:

As a Senior Data Platform 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.

In Your New Role:

As a Senior Data Platform Engineer you will join an impactful, multi-functional team of data scientists, data analysts, data engineers, and backend engineers who are dedicated to creating a data-led 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. You will utilize your experience and create appropriate solutions and tools to solve complex engineering problems related to data, orchestration, and scale for services related to ingestion from external and internal sources.

StyleSeat is a rapidly scaling company making this the best environment to take on ownership of your role, as well as learn how to grow with a company. Our engineering team consists of developers from a wide array of backgrounds. Our team is a tight-knit, friendly group of engineers, who are dedicated to learning from 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 You’ll Do:

• Build systems in event-driven or streaming architectures using systems such as Kafka/Kinesis, RabbitMQ, NATS, and AWS SNS/SQS


• Develop and further develop RESTful APIs, as well as configure Kafka message brokers and Kinesis streams


• Develop scalable data ingestion processing pipelines for data sources containing structured and unstructured data


• Monitor and optimize key infrastructure components such as Databases, EC2 clusters, EKS clusters, Docker containers and other aspects of the stack


• Act as a bridge between the Data Engineering team and the wider Engineering organization


• Work in an Agile manner with Data Engineers, Data Scientists and Machine Learning Engineers to understand and discover the potential business value of new and existing Data Sets and help put those discoveries into production

What You Can Bring to the Table:

• 5+ years as a Backend or Software Engineer (Python or Java Required)




• 2+ years of experience with AWS Data Infrastructure, including Kafka, Kinesis & S3


• 2+ years building large scale distributed micro-services with REST and encapsulated Data layers




• Experience designing, developing, and owning pipelines that deliver data with measurable quality under a pre-defined SLA


• Proficiency with Python, Bash and other scripting languages

• Medium-to-high level of Python or Java proficiency (understanding of data structures in a functional and modular capacity)

• 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 any of the following message / file formats: Parquet, Avro, Protocol Buffer


• Experience with Redis, Cassandra, MongoDB or similar NoSQL databases