At a Glance
- Tasks: Design and maintain data systems processing billions of rows daily for trading decisions.
- Company: Join a leading firm at the forefront of data engineering and trading technology.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborate with diverse teams and tackle exciting challenges in the trading sector.
- Why this job: Make a real impact by optimising data pipelines in a dynamic, fast-paced environment.
- Qualifications: 9+ years in data engineering with strong skills in SQL, Python, and cloud technologies.
The predicted salary is between 60000 - 80000 £ per year.
The Data Engineering team is responsible for a fundamental data system processing 50+ billion rows of data per day and feeding directly into trading decisions. The Data Engineer will be designing, implementing and maintaining this system, keeping it reliable, resilient, and low-latency.
We are looking for a highly technical engineer with strong experience working in MPP platforms and/or Spark, "big data" (e.g., weather forecasts, AIS pings, satellite imagery), and developing resilient and reliable data pipelines. You will be responsible for data pipelines end to end: acquisition, loading, transformation, implementing business rules/analytics, and delivery to the end user (trading desks / data science / AI). You will partner closely with business stakeholders and engineering teams to understand their data requirements and deliver the necessary data infrastructure to support their activities.
Given our scale, you should bring a deep focus on performance optimisation, improving data access times and reducing latency. This role will require strong coding skills in SQL and Python, and a deep understanding of how to leverage the AWS stack. Strong communication is essential: you should be comfortable translating technical concepts to non-technical users, as well as turning business requirements into clear, actionable technical designs.
Qualifications
- Essential
- 9+ years in the data engineering space
- Proficient with MPP Databases (Snowflake (preferred), Redshift, Big Query, Azure DW) and/or Apache Spark
- Proficient at building resilient data pipelines for large datasets
- Deep AWS or cloud understanding across core and extended services.
- 5+ years experience working with at least 3 of the following: ECS, EKS, Lambda, DynamoDB, Kinesis, AWS Batch, ElasticSearch/OpenSearch, EMR, Athena, Docker/Kubernetes
- Proficient with Python and SQL, and with good experience with data modelling
- Experience with modern orchestration tools (Airflow / Prefect / Dagster / similar)
- Comfortable working in a dynamic environment with evolving requirements
Desirable
- Exposure to trading and/or commodity business
- DBT experience
- Infrastructure as Code (Terraform, Cloud Formation, Ansible, Serverless)
- CI/CD Pipelines (Jenkins / GIT / BitBucket Pipelines / similar)
- Database/SQL tuning skills
- Basic data science concepts
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like jobr.pro.
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We think you need these skills to ace Data Engineer
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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Craft a Tailored Cover Letter:For a full-time role at jobr.pro, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at jobr.pro. 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 jobr.pro
✨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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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at jobr.pro!
✨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.
