At a Glance
- Tasks: Design and build data processing pipelines using cutting-edge tech to handle terabytes of data.
- Company: Vortexa, a fast-growing tech company revolutionising the energy industry with AI.
- Benefits: Flexible working, private health insurance, equity options, and a vibrant team culture.
- Why this job: Join a dynamic startup and make a real impact in the energy sector with innovative solutions.
- Qualifications: Experience in AWS, K8s, Python, and Java; strong software engineering fundamentals.
- Other info: Collaborative environment with opportunities for personal growth and continuous learning.
The predicted salary is between 48000 - 84000 ÂŁ per year.
Vortexa is a fast‑growing international technology business founded to solve the immense information gap that exists in the energy industry. By using massive amounts of new satellite data and pioneering work in artificial intelligence, Vortexa creates an unprecedented view on the global seaborne energy flows in real‑time, bringing transparency and efficiency to the energy markets and society as a whole.
Role
Processing thousands of rich data points per second from many and vastly different external sources, moving terabytes of data while processing it in real‑time, running complex prediction and forecasting AI models while coupling their output into a hybrid human‑machine data refinement process and presenting the result through a nimble low‑latency SaaS solution used by customers around the globe is no small feat of science and engineering. This processing requires models that can survive the scrutiny of industry experts, data analysts and traders, with the performance, stability, latency and agility a fast‑moving startup influencing multi‑$m transactions requires.
The Data Production Team is responsible for all of Vortexa\’s data. It ranges from mixing raw satellite data from 600,000 vessels with rich but incomplete text data, to generating high‑value forecasts such as the vessel destination, cargo onboard, ship‑to‑ship transfer detection, dark vessels, congestion, future prices, etc.
The team has built a variety of procedural, statistical and machine learning models that enabled us to provide the most accurate and comprehensive view of energy flows. We take pride in applying cutting‑edge research to real‑world problems in a robust, long‑lasting and maintainable way. The quality of our data is continuously benchmarked and assessed by experienced in‑house market and data analysts to ensure the quality of our predictions.
You\’ll be instrumental in designing and building infrastructure and applications to propel the design, deployment, and benchmarking of existing and new pipelines and ML models. Working with software and data engineers, data scientists and market analysts, you\’ll help bridge the gap between scientific experiments and commercial products by ensuring 100% uptime and bullet‑proof fault‑tolerance of every component of the team\’s data pipelines.
Qualifications
- Experienced in building and deploying distributed scalable backend data processing pipelines that can go through terabytes of data daily using AWS, K8s, and Airflow.
- With solid software engineering fundamentals, fluent in both Java and Python (with Rust good to have).
- Knowledgeable about data lake systems like Athena, and big data storage formats like Parquet, HDF5, ORC, with a focus on data ingestion.
- Driven by working in an intellectually engaging environment with the top minds in the industry, where constructive and friendly challenges and debates are encouraged, not avoided.
- Excited about working in a start‑up environment: not afraid of challenges, excited to bring new ideas to production, and a positive can‑do will‑do person, not afraid to push the boundaries of your job role.
- Passionate about coaching developers, helping them improve their skills and grow their careers.
- Deep experience of the full software development life cycle (SDLC), including technical design, coding standards, code review, source control, build, test, deploy, and operations.
Preferred Skills
- Have experience with Apache Kafka and streaming frameworks, e.g., Flink.
- Familiar with observability principles such as logging, monitoring, and tracing.
- Have experience with web scraping technologies and information extraction.
- A vibrant, diverse company pushing ourselves and the technology to deliver beyond the cutting edge.
- A team of motivated characters and top minds striving to be the best at what we do at all times.
- Constantly learning and exploring new tools and technologies.
- Acting as company owners (all Vortexa staff have equity options) – in a business‑savvy and responsible way.
- Motivated by being collaborative, working and achieving together.
- A flexible working policy – accommodating both remote & home working, with regular staff events.
- Private Health Insurance offered via Vitality to help you look after your physical health.
- Global Volunteering Policy to help you \’do good\’ and feel better.
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Data Engineer (Multiple Roles) - AI SaaS employer: Vortexa
Contact Detail:
Vortexa Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer (Multiple Roles) - AI SaaS
✨Tip Number 1
Network like a pro! Get out there and connect with people in the industry. Attend meetups, webinars, or even just grab a coffee with someone who works at Vortexa. Building relationships can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or contributions to open-source, make sure to highlight them. Create a GitHub profile that showcases your work with data pipelines or machine learning models. It’s a great way to demonstrate your expertise.
✨Tip Number 3
Prepare for the interview like it’s the Super Bowl! Research Vortexa’s products and the tech stack they use. Be ready to discuss how your experience aligns with their needs, especially around AWS, K8s, and data processing. Confidence is key!
✨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, it shows you’re genuinely interested in joining the team at Vortexa. Let’s get you that dream job!
We think you need these skills to ace Data Engineer (Multiple Roles) - AI SaaS
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Data Engineer role. Highlight your experience with AWS, K8s, and data processing pipelines to show us you’re the right fit!
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about working at Vortexa. Share your passion for data engineering and how you can contribute to our mission of bringing transparency to the energy markets.
Showcase Your Projects: If you've worked on relevant projects, don’t hold back! Include links or descriptions of your work with machine learning models or data pipelines. We love seeing real-world applications of your skills.
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 shows us you’re keen to join our team!
How to prepare for a job interview at Vortexa
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like AWS, K8s, and Airflow. Brush up on your Java and Python skills, and be ready to discuss how you've used these tools in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in building data pipelines or working with large datasets. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your analytical thinking.
✨Demonstrate Your Passion for Learning
Vortexa values continuous learning, so share examples of how you’ve kept up with industry trends or learned new technologies. Mention any relevant courses, certifications, or personal projects that showcase your commitment to growth.
✨Be Ready for Collaborative Discussions
Since the role involves working closely with various teams, prepare to discuss how you handle constructive feedback and collaboration. Think of examples where you’ve successfully worked in a team to solve complex problems or improve processes.