At a Glance
- Tasks: Lead the development of scalable data pipelines and integrate complex data sources.
- Company: Innovative SaaS business transforming commercial data management.
- Benefits: Up to £80,000 salary, annual bonus, free gym, and pension scheme.
- Other info: Dynamic role with opportunities for leadership and mentorship.
- Why this job: Join a leading tech company and make an impact in data innovation.
- Qualifications: Degree in STEM and 5+ years in Data Engineering with strong Python skills.
The predicted salary is between 80000 - 80000 £ per year.
Senior Data Engineer - Python, SQL, Airflow, Azure, Pyspark, Kubernetes
Benefits: Excellent benefits package including annual bonus, free onsite gym, and company pension scheme.
About the Role
We are seeking an experienced Senior Data Engineer to join an innovative Saa S business that is transforming the way commercial data is managed and utilised.
In this role, you will lead the development of scalable data pipelines and integrate complex data sources into a modern cloud-based platform.
About the Company
Our client is a leading technology business focused on transforming commercial Asset management through data and innovation.
Their Saa S platform consolidates and reconciles data across financial, operational, and leasing functions, providing a single source of truth for asset, fund, and portfolio performance.
Key Responsibilities
- Data Pipeline Development
- Data Platform Management
- Stakeholder Collaboration
- Leadership & Mentorship
- Technology Stack
- Data Processing: Apache Spark / Py Spark
- Workflow Orchestration: Apache Airflow
- Containerisation: Kubernetes
- Cloud Platform: Microsoft Azure
- Data APIs & Semantic Layer: Cube JS
- Programming Languages: Python, SQLEssential Skills & Experience
Essential Skills & Experience
- Degree in a STEM discipline, ideally Computer Science, Software Engineering, or a related field.
- Minimum of 5 years' experience within Data Engineering, Data Warehousing, or a similar role.
- Strong experience with Python and Py Spark.
- Excellent SQL skills.
- Experience with data orchestration tools such as Airflow, Azure Data Factory (ADF), SSIS, or similar.
- Solid understanding of data modelling principles and modern data warehouse architectures.
- Experience designing and building scalable, production-grade data pipelines.
Desirable Experience
- Experience working within cloud-native Azure environments.
- Knowledge of modern analytics platforms and semantic data layers.
- Previous mentoring or technical leadership experience.
Senior Data Engineer - Python, Airflow, Pyspark, SQL in Dundee employer: TRIA
Tria is an exceptional employer that fosters a dynamic work culture, offering hybrid working arrangements that promote work-life balance. With a strong focus on employee growth and development, Tria provides opportunities for professional advancement while leading transformative projects in the North West. Join a team that values innovation and collaboration, ensuring you play a pivotal role in shaping the future of enterprise infrastructure.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer - Python, Airflow, Pyspark, SQL in Dundee
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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 TRIA.
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We think you need these skills to ace Senior Data Engineer - Python, Airflow, Pyspark, SQL in Dundee
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at TRIA, 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 TRIA. 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 TRIA
✨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!
✨Showcase Your Projects
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 TRIA!
✨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.
