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Our data platform team is dedicated to establishing a robust data infrastructure, facilitating easy access to quality, reliable, and timely data for reporting, analytics, and actionable insights. We focus on designing and building a sustainable and scalable data architecture, treating data as a core corporate asset. Our efforts also include process improvement, governance enhancement, and addressing application, functional, and reporting needs. We value teammates who are helpful, respectful, communicate openly, and prioritize the best interests of our users. Operating across various cities and timezones in the US, our team fosters collaboration to deliver work that brings pride and fulfillment.

We are looking for a thoughtful and experienced data engineer to help grow a suite of systems and tools written primarily in Python. The ideal candidate will have a deep understanding of modern data engineering concepts and will have shipped or supported code and infrastructure with a user base in the millions and datasets with billions of records. The candidate will be routinely implementing features, fixing bugs, performing maintenance, consulting with product managers, and troubleshooting problems. Changes you make will be accompanied by tests to confirm desired behavior. Code reviews, in the form of pull requests reviewed by peers, are a regular and expected part of the job as well.

Salary Range: $110,000 - $138,000

• Develop data pipelines or features related to data ingestion, transformation, or storage using Python and relational databases (e.g., PostgreSQL) or cloud-based data warehousing (e.g.,BigQuery)
• Collaborate with product managers to define clear requirements, deliverables, and milestones
• Team up with other groups within OReilly (e.g. data science or machine learning) to leverage experience and consult on data engineering best practices
• Review a pull request from a coworker and pair on a tricky problem
• Provide a consistent and reliable estimate to assess risk for a project manager
• Learn about a new technology or paper and present it to the team
• Identify opportunities to improve our pipelines through research and proof-of-concepts
• Help QA and troubleshoot a pesky production problem
• Participate in agile process and scrum ceremonies

What Youll Have

• 3+ years of professional data engineering experience (equivalent education and/or experience may be considered)
• 2+ year experience of working in an agile environment
• Proficiency in building highly scalable ETL and streaming-based data pipelines using Google Cloud Platform services and products
• Experience in building data pipelines using Docker
• Experience in building data pipelines using tools such as Talend, Fivetran
• Proficiency in large scale data platforms and data processing systems such as S3, GCS, Google BigQuery and Amazon Redshift
• Excellent Python and PostgreSQL development and debugging skills
• Experience building systems to retrieve and aggregate data from event-driven messaging frameworks (e.g. RabbitMQ and Pub/Sub)
• Experience with deployment tools such as Jenkins to build automated CI/CD pipelines
• Strong drive to experiment, learn and improve your skills
• Respect for the craftyou write self-documenting code with modern techniques
• Great written communication skillswe do a lot of work asynchronously in Slack and Google Docs
• Empathy for our usersa willingness to spend time understanding their needs and difficulties is central to the team
• Desire to be part of a compact, fun, and hard-working team

• Experience with Google Cloud Dataflow/Apache Beam
• Experience with Django RESTful endpoints
• Experience working in a distributed team
• Knowledge and experience with machine learning pipelines
• Contributions to open source projects
• Knack for benchmarking and optimization