At a Glance
- Tasks: Build data pipelines and ensure data quality through rigorous testing.
- Company: Obsidian, a leader in data engineering innovation.
- Benefits: Competitive hourly pay ranging from $90 to $125 based on experience.
- Other info: Collaborative environment with opportunities for professional growth.
- Why this job: Join a cutting-edge team and make an impact in data engineering.
- Qualifications: BS or MS in Computer Science and experience in data engineering.
The predicted salary is between 72000 - 100000 £ per year.
Obsidian is looking for a Data Engineering Expert to advance frontier agent evaluations in data engineering. Your role will involve building long-horizon pipeline tasks, utilizing ETL/ELT models, and ensuring data quality through rigorous testing. You will collaborate closely on scenarios involving pipeline orchestration and warehouse design.
The ideal candidate has a BS or MS in Computer Science, experience in data engineering, and excellent written communication skills. Compensation ranges from $90 to $125 per hour based on experience.
Analytics Engineer: Build Data Pipelines & DAGs employer: Obsidian
At Obsidian, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to excel in their roles. As an Analytics Engineer, you will have access to competitive compensation, opportunities for professional growth, and the chance to work on cutting-edge data engineering projects that make a real impact. Our commitment to employee development and a supportive environment makes us an exceptional employer for those seeking meaningful and rewarding careers in data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer: Build Data Pipelines & DAGs
✨Tip Number 1
Network like a pro! Reach out to folks in the data engineering field on LinkedIn or at meetups. We can’t stress enough how personal connections can open doors for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data pipelines and any projects you've worked on. This gives potential employers a taste of what you can do, and we all know actions speak louder than words.
✨Tip Number 3
Prepare for those interviews! Brush up on your ETL/ELT models and be ready to discuss your approach to ensuring data quality. We recommend practising common interview questions with a friend or even in front of the mirror.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Analytics Engineer: Build Data Pipelines & DAGs
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in data engineering and any relevant projects you've worked on. We want to see how your skills align with the role, so don’t be shy about showcasing your expertise!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our team. Keep it concise but impactful – we love a good story!
Showcase Your Communication Skills:Since excellent written communication is key for this role, make sure your application is clear and well-structured. We appreciate a good flow, so take the time to proofread and polish your writing.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Obsidian
✨Know Your Data Engineering Basics
Make sure you brush up on your data engineering fundamentals, especially around ETL/ELT models. Be ready to discuss how you've built data pipelines in the past and the challenges you've faced. This will show that you have the technical chops for the role.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex data problems. Think about scenarios where you had to ensure data quality or orchestrate pipelines. This will demonstrate your analytical thinking and ability to handle real-world challenges.
✨Communicate Clearly and Effectively
Since excellent written communication skills are a must, practice explaining your past projects succinctly. Use clear language and avoid jargon unless necessary. This will help you connect with the interviewers and convey your ideas effectively.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's data practices and future projects. This shows your genuine interest in the role and helps you gauge if the company is the right fit for you. Plus, it gives you a chance to engage in a meaningful conversation.
