At a Glance
- Tasks: Manage and optimise self-hosted data infrastructure while designing scalable systems.
- Company: Join a forward-thinking tech company focused on innovative data solutions.
- Benefits: Attractive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative team culture with a focus on technical excellence and career advancement.
- Why this job: Be a key player in shaping the future of data engineering with cutting-edge technology.
- Qualifications: 5+ years in data engineering with expertise in self-hosted environments and database management.
The predicted salary is between 60000 - 80000 £ per year.
We are seeking a Principle Data Engineer with extensive experience in data infrastructure to join our team. This position is looking for someone to manage our self-hosted and on-premise data stacks. We need a proactive, solutions-oriented engineer who can rapidly master our current self-hosted systems, identify performance bottlenecks, and design our future infrastructure to adapt to the company needs. In this role, you will own the technical architecture, set engineering standards, and translate complex business requirements into reliable, scalable data systems.
RESPONSIBILITIES
- Infrastructure Strategy & Ownership: Manage and optimize self-hosted database and data warehousing solutions. Oversee performance tuning, hardware planning, and OS-level monitoring for large-scale data systems.
- Pipeline Architecture: Design, develop, and maintain robust ETL/ELT pipelines for data ingestion and processing from varied, complex data sources.
- Technical Execution: Drive large-scale technical projects from design to delivery, making key architectural decisions and managing technical risk.
- Engineering Standards: Establish code and architecture review standards, championing engineering best practices across the team.
- Data Governance & Security: Ensure infrastructure and pipelines strictly comply with government policies, data privacy, and GDPR regulations.
- Collaboration: Partner with data scientists, analysts, and business stakeholders to translate complex data requirements into scalable infrastructure.
REQUIREMENTS
- Experience: 5+ years of professional experience in data engineering, database administration, or backend infrastructure, with a strong, proven focus on managing self-hosted/on-premise environments rather than exclusively cloud-managed services.
- Deep Database Foundations: Extensive hands-on experience managing and optimizing database management systems, specifically PostgreSQL and MongoDB (both relational and NoSQL ecosystems).
- Self-Hosted Mastery: Proven experience setting up, maintaining, and tuning self-hosted/on-premise databases and data warehouses.
- System Environment: Strong proficiency in SQL and deep comfort working within Linux/Unix environments (command line, shell scripting, system performance tuning).
- Data Orchestration: Hands-on experience with modern ETL orchestration tools such as Dagster or Airflow.
- Data Modeling: In-depth knowledge of core data warehousing concepts and relational data modeling.
- Technical Ownership: Proven track record of owning complex technical systems, driving implementation, and ensuring architectural standards are met.
- Communication: Ability to clearly communicate technical decisions, trade-offs, and infrastructure constraints to both engineering teams and non-technical stakeholders.
Applicants must be legally eligible to work in Canada at the time of application. Salary Range: $115,000 - $125,000
Principle Data Engineer employer: Digital Extremes
Join a forward-thinking company that values innovation and technical excellence, where as a Principal Data Engineer, you will play a pivotal role in shaping our data infrastructure. We foster a collaborative work culture that encourages continuous learning and professional growth, offering competitive salaries and comprehensive benefits. Located in Canada, we provide a unique opportunity to work with cutting-edge technologies while ensuring compliance with data governance and security standards.
StudySmarter Expert Advice🤫
We think this is how you could land Principle Data Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Digital Extremes!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Principle Data Engineer at Digital Extremes.
✨Leverage Professional Networks
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 Digital Extremes.
✨Apply Directly through Our Website
When you find a suitable opening like Principle Data Engineer at Digital Extremes, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Principle 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!
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 Digital Extremes, 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 Digital Extremes. 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 Digital Extremes
✨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 Digital Extremes!
✨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.
