At a Glance
- Tasks: Design and develop scalable data systems while influencing our platform's reliability and growth.
- Company: Join Astronomer, a leader in cloud infrastructure and open-source software.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Be part of a diverse team that values innovation and collaboration.
- Why this job: Make a real impact on how global organisations manage data pipelines at scale.
- Qualifications: Hands-on experience with Postgres, data systems, and strong Golang skills required.
The predicted salary is between 36000 - 60000 £ per year.
About this role: At Astronomer, we are redefining how companies run Apache Airflow at scale. Our R&D organization is home to some of the most innovative minds in cloud infrastructure and open-source software. We are looking to add a world-class Staff+ level engineer to our team, to set out our Data and Database story as we level up our platform's Reliability, cost profile and growth trajectory.
You get to go in at the ground level of how our production infrastructure is designed, built, tested and deployed. Your work will directly influence how we build Astro, Observe and our IDE product, as well as how global organizations orchestrate data pipelines at scale—making them faster, more reliable, and easier to manage.
What you get to do:
- Be a subject-matter expert in how we treat Data at scale. Astronomer has a number of different databases and data sources at work providing our platform, and our needs are evolving. We are looking for a database and data platforms expert to map out how we store, retrieve, keep safe, and otherwise be responsible curators of ours and customers' data.
- Astronomer's stack is fairly heavily Postgres-based, with some blob storage and some specialised options for certain data types - you get to recommend, design and later lead building the data systems that will help us continue to scale. This is very much a technical role; you'll be just as active in building these systems and ensuring they're fit for purpose as specifying and designing.
- You'll be at the forefront of how we work together as a Platform Engineering team and an R&D group more broadly.
Blaze a Trail: Own and develop our Database and overall data strategy and practice, with sponsorship and responsibility to match – this role reports directly to the VP of Reliability.
Be an Owner: Be directly involved in decision-making on what we work on, as well as how we work on it. Make promises, and keep them.
Do Sensible Things: Be directly involved in determining how our platform works. Make build vs. buy assessments, and advocate for the right tools for the right job when it comes to data.
Garage Door Open: Create and maintain comprehensive internal documentation and decision records for systems and processes, ensuring clarity and accessibility. Participate in Architectural forums and discussion and make principled, open decisions.
What you bring to the role:
- Hands-on experience designing, developing, and scaling production infrastructure.
- Extensive knowledge of Postgres and Postgres-like cloud offerings (AWS, GCP, Azure).
- Extensive and recent experience with building low-level data systems and/or managed data platforms.
- In-depth knowledge and practical experience of the product and technology space in Database and related fields (Blob, NoSQL, Timeseries, Graph, Vector).
- Experience defining requirements and making and justifying technology choices around the data space.
- Strong experience in Non-Abstract Systems design and implementation.
- Strong proficiency in Golang and in-depth experience with Kubernetes.
- Strong communication skills, both written and verbal, with experience in working with a globally distributed team in delivery.
Bonus points if you have:
- Experience working with Spanner, AlloyDB and/or other cloud-native databases, including hands-on experience with provisioning, development practices and migration of data.
- Experience working on a SaaS/PaaS product across multiple cloud providers.
- Experience building internal data platforms from cloud-native component parts - we have a healthy mix of build vs. buy; sometimes building is the right choice.
- Experience with Apache Airflow.
At Astronomer, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Staff Software Engineer, Data in London employer: Astronomer
Astronomer is an excellent employer that fosters a collaborative and innovative work culture, where your contributions directly impact the development of cutting-edge cloud infrastructure. Located in the vibrant city of London, we offer competitive benefits, opportunities for professional growth, and a supportive environment that encourages continuous learning and development. Join us to be part of a dynamic team dedicated to operational excellence and reliability in our products.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Software Engineer, Data in London
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We think you need these skills to ace Staff Software Engineer, Data in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Astronomer. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Astronomer
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.