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- Tasks: Transform data into valuable insights and develop scalable data pipelines.
- Company: Join a leading tech company focused on innovation and analytics.
- Benefits: Enjoy competitive pay, flexible work options, and growth opportunities.
- Why this job: Make an impact by driving analytics projects and shaping data strategies.
- Qualifications: 5+ years experience in analytics with strong SQL and Python skills.
- Other info: Hybrid role with a dynamic team and exciting career prospects.
The predicted salary is between 36000 - 60000 £ per year.
The Analytics Engineer is responsible for acting strategically and technically in transforming data into valuable business insights. Core responsibilities include:
- Developing and maintaining efficient, scalable data pipelines, ensuring data quality, integrity, and availability for analysis and decision-making.
- Collaborating with the data architecture team to design and implement analytics solutions, aligning both technical and business requirements.
- Supporting the continuous development and evolution of the analytics and machine learning platform, contributing to the integration of new technologies and best practices.
- Leading technical initiatives in analytics projects, serving as a reference in resolving technical challenges and supporting the team throughout the entire project lifecycle.
- Actively participating in discussions about the evolution of the data area, proposing new approaches, tools, and projects focused on innovation and solving the problems of internal clients.
- Ensuring the implementation of DataOps and CI/CD practices, optimizing the development, testing, and operation of data flows.
Qualifications
Basic Qualifications:
- 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
Preferred Qualifications:
- 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
- Expertise in SQL for large-scale data manipulation and analysis.
- Proficiency in Python, focused on automation of pipelines, data analysis, and API integration.
- Advanced experience with Databricks, including notebooks, Delta Lake, and integration with various data sources.
- Solid knowledge of AWS, especially the following services: Glue (serverless ETL), Athena (serverless SQL queries), S3 (data storage), Lambda (serverless function execution).
- Practical knowledge of Terraform for provisioning and managing infrastructure as code (IaC).
- Experience with Git for code versioning and team collaboration.
- Knowledge in BI and data modeling, with the ability to structure data for visualizations and strategic analyses.
- Experience with data orchestration (e.g., Airflow).
- Experience working in environments with high data volume and performance requirements.
- Experience with modern data architecture (Data Lakehouse, medallion architecture).
This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager.
Staff Analytics Engineer in London employer: Pismo
Contact Detail:
Pismo Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Analytics Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the analytics field and let them know you're on the hunt for a Staff Analytics Engineer role. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving SQL, Python, and data pipelines. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with Databricks, AWS, and DataOps practices, as these are key areas for the role.
✨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 to connect with us directly.
We think you need these skills to ace Staff Analytics Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Staff Analytics Engineer role. Highlight your experience with SQL, Python, and any relevant data architecture projects. We want to see how your skills align with our needs!
Showcase Your Projects: Include specific examples of projects where you've developed data pipelines or worked with analytics solutions. We love seeing real-world applications of your skills, so don’t hold back on the details!
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for easy reading and make sure to emphasise your key achievements. We appreciate straightforward communication!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Pismo
✨Know Your Tech Inside Out
Make sure you brush up on your SQL, Python, and AWS skills. Be ready to discuss how you've used these technologies in past projects, especially in relation to data pipelines and analytics solutions. The more specific examples you can provide, the better!
✨Showcase Your Problem-Solving Skills
Prepare to talk about technical challenges you've faced and how you resolved them. Companies love candidates who can think critically and propose innovative solutions, so have a few stories ready that highlight your analytical mindset.
✨Understand DataOps and CI/CD Practices
Since this role involves optimising data flows, be prepared to discuss your experience with DataOps and CI/CD. Familiarise yourself with best practices and be ready to explain how you've implemented these in previous roles.
✨Engage in the Conversation
Interviews are a two-way street! Prepare thoughtful questions about the company's data strategy and future projects. This shows your genuine interest in the role and helps you assess if it's the right fit for you.
