Hims and Hers offers a modern approach to health and wellness. Our mission is to eliminate stigmas and make it easier for people to access care and treatment for the conditions that impact their daily lives. That starts with creating an open and honest culture of care that is accessible for everyone, no matter who you are or where you live. Since launching in November 2017, we’ve raised over $200MM in funding and are one of the fastest growing direct-to-consumer brands in history.
Hims and Hers is seeking an experienced Infrastructure Data Engineer to join the team. You will come on to a small team and immediately make a concrete impact on the growth of the business. This role would be responsible for upgrading data infrastructure and business intelligence tools, optimize data pipelines, implement new data engineering solutions as well as enable robust business and customer analytics to strengthen the efficiency and dependability of the way we make decisions.
- Take data infrastructure to the next level by solidifying and automating our tools and frameworks in GCP and Aptible such as Airflow, Cloud Composer, Dataflow, Docker, and others
- Lead efforts to get our data freshness from 2-hours to near real-time
- Templatize pipelines and processes including internal, external, and DS/ML pipelines
- Implement CI and data integrity frameworks to verify and ensure data and process consistency
- Unify notification and escalation framework using Slack and Pagerduty to define and support data SLA.
- Be part of talented data engineering team that ships new code every day in a fast-paced work environment
Experience & Skills:
- 7+ years experience as data engineer designing and deploying data infrastructure in a startup environment
- Mastery of processing data using Airflow in GCP (preferred) or AWS
- Deep understanding of modern ETL tools and Big Data systems and ability to articulate their pros and cons.
- Expertise in designing, deploying, and automating data infrastructure frameworks in Python and SQL
- Fluency in developing data pipelines in Python
- Proficiency in writing SQL for data transformation
- Expertise in overall software engineering best practices as they apply to data engineering domain