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- Tasks: Join us as a Senior Python Engineer, focusing on backend development and containerized web services.
- Company: Be part of a dynamic team in London, working on innovative projects in the commodities trading sector.
- Benefits: Enjoy a hybrid work model with flexible hours and opportunities for professional growth.
- Why this job: This role offers hands-on experience with cutting-edge technologies and a chance to impact the energy market.
- Qualifications: Must have extensive Python experience, knowledge of Azure, and familiarity with data management and trading domains.
- Other info: This is a 2-year contract position, ideal for those looking to advance their careers in tech.
The predicted salary is between 48000 - 72000 £ per year.
We are looking for a Senior Python Developer. This is a hybrid role based in London on a 2 year contract.
Must Haves:
- Experience in Python backend engineering
- Experienced in development of elastic containerized web services in Azure
- Proven experience designing and implementing automatic deployment pipelines
- Experience with low latency data aggregation solutions
- Commodities Trading domain experience, ideally Gas/Power Trading knowledge (Short-term trading, physical assets trading (CCGT, Wind, Solar, Battery))
- Experience with SRE Observability
- Expertise in prototyping, solution design and delivering end-to-end projects; able to work/demo on POC involving new technologies
- Champion in working with any OLTP/OLAP databases
- Highly experienced in building and optimizing complex queries
- Highly experienced with data manipulation, processing and extracting value from large, disconnected datasets
- Experienced working with NoSQL databases, big data sets and big data technologies
- Hands-on experience designing data foundation initiatives like data modelling, data quality, data governance, data maturity assessments and data strategy in support of the key business stakeholders
- Proven experience in working with source control technologies (such as GITHUB, Azure DevOps)
- Experience in working with AGILE, KANBAN methodologies
Pluses:
- ETRM experience, preferably Endur Risk - Modelling & understanding of risk & risk management
- Quantitative Skills - Statistical methods to check and investigate numerical data for practical insights
- Commodity Modelling - Energy (power, gas, environmental products) and meteorology
- Experience in project management, running a scrum team
- Experience working with BPC, Planning
- Exposure to working with external technical ecosystem
- Lead experience
Sr Python Engineer employer: Insight Global
Contact Detail:
Insight Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Sr Python Engineer
✨Tip Number 1
Network with professionals in the commodities trading sector, especially those with experience in gas and power trading. Attend industry meetups or webinars to connect with potential colleagues and learn more about the specific challenges they face.
✨Tip Number 2
Familiarise yourself with Azure's container services and deployment pipelines. Consider building a small project that showcases your ability to create elastic containerised web services, as this will demonstrate your hands-on experience during interviews.
✨Tip Number 3
Brush up on your knowledge of OLTP/OLAP databases and practice writing complex queries. Being able to discuss your approach to data manipulation and optimisation will set you apart from other candidates.
✨Tip Number 4
Showcase your experience with Agile methodologies by preparing examples of how you've successfully led scrum teams or managed projects. Highlighting your leadership skills can make a significant impact during the interview process.
We think you need these skills to ace Sr Python Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Python backend engineering and any relevant projects you've worked on, especially those involving elastic containerized web services in Azure. Use keywords from the job description to ensure your application stands out.
Craft a Compelling Cover Letter: In your cover letter, emphasise your experience with low latency data aggregation solutions and your knowledge of the commodities trading domain. Share specific examples of how you've designed and implemented automatic deployment pipelines.
Showcase Relevant Projects: If you have experience with SRE observability or have worked on prototyping and solution design, include these projects in your application. Highlight your ability to deliver end-to-end projects and any demos you've conducted using new technologies.
Highlight Your Methodologies: Mention your familiarity with AGILE and KANBAN methodologies in your application. If you have experience working with source control technologies like GITHUB or Azure DevOps, be sure to include that as well, as it aligns with the company's requirements.
How to prepare for a job interview at Insight Global
✨Showcase Your Python Expertise
Be prepared to discuss your experience with Python backend engineering in detail. Highlight specific projects where you've developed elastic containerised web services, and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Your Knowledge of Azure
Since this role involves working with Azure, make sure you can talk about your experience with automatic deployment pipelines and any relevant tools you've used. Providing examples of how you've implemented these in past projects will impress the interviewers.
✨Discuss Your Experience in Commodities Trading
If you have experience in the commodities trading domain, especially in Gas/Power Trading, be sure to mention it. Discuss any specific knowledge you have regarding short-term trading and physical assets trading, as this will be a significant advantage.
✨Highlight Your Data Skills
Given the emphasis on data manipulation and processing, prepare to discuss your experience with OLTP/OLAP databases and NoSQL technologies. Be ready to share examples of complex queries you've built and how you've extracted value from large datasets.
