Software Engineer, Data Infrastructure (Remote San Francisco, CA)
This job is no longer accepting applications.
As one of Benchling’s first data engineers, you’ll join a rapidly growing, world-class engineering team and form the foundation of our data pillar, encompassing internal analytics and product data science. You will build the next generation of our data infrastructure and enable the company to answer key questions and make better decisions. Benchling is growing really quickly, and you’ll be setting the bar for high quality data and a metrics-driven culture as we scale.
In addition, Benchling is building its own analytics products for customers to enable asking operational and experimental questions of Benchling's R&D platform. You’ll serve as a key input and work closely with the Product team to bring best principles and techniques to our customers and the industry at large.
YOU MIGHT WORK ON
- Architect and build Benchling’s data platform for internal analytics, product data science, machine learning, and growth engineering.
- Define and design data transformations and pipelines for these cross-functional datasets, while ensuring that data integrity and data privacy are first-class concerns regarded proactively, instead of reactively.
- Define the right Service Level Objectives for the data pipelines, and optimize their performance.
- Architect and implement scalable APIs for internal and external customers to access data in our warehouse.
- Engineer changes to Benchling’s products to provide richer instrumentation that will lead to the development of new data products and systems for either internal use cases or new customer-facing products.
- Work closely with Sales, Marketing, Customer Experience, Product, and Engineering to establish best practices around usage of our data platform.
- BS or MS degree in Computer Science or a related technical field
- 5+ years of industry experience designing and building data processing systems
- Driven by creating positive impact for our customers and Benchling's business, and ultimately accelerating the pace of research in the Life Sciences
- Strong communicator with both words and data - you understand what it takes to go from raw data to something a human understands
- Comfortable with complexity in the short term but can build towards simplicity in the long term
- Experience with Spark, Airflow, MapReduce, or other open-source or commercial software related to data processing
- Experience with data analytics and warehouse solutions such as Snowflake, AWS Redshift, etc
- Comfortable with SQL and a scripting language (such as Python)
- Plus: comfortable with R, Matlab, or other languages / frameworks used for computation across large datasets
- Plus: experience with data sets in any of the business or operational domains listed above (Sales, Marketing, Customer Experience, etc.)
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