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- Tasks: Develop and implement automated solutions for data integration and quality control.
- Company: Join a forward-thinking organisation focused on data science and analytics.
- Benefits: Enjoy flexible work options, competitive pay, and opportunities for professional growth.
- Why this job: Be part of a collaborative team driving innovation in data management and analytics.
- Qualifications: Proficiency in SQL, Python, and cloud platforms; strong problem-solving skills required.
- Other info: Stay ahead with emerging technologies and best practices in DataOps.
The predicted salary is between 36000 - 60000 £ per year.
As a DataOps Engineer, your responsibilities will span the development and implementation of automated solutions for data integration, quality control, and continuous delivery. This role demands a solid grounding in software engineering principles, fluency in programming languages such as Python or Scala, and an adeptness with DevOps tools. You'll play a crucial role in constructing and maintaining sophisticated data pipelines that support the organization's data science and analytics ambitions.
Collaboration is a cornerstone of this position. You will work closely with teams across the organization, assimilating their data requirements and challenges, and crafting agile, robust data solutions. Your efforts in implementing best practices in DataOps will aim to eliminate bottlenecks, elevate data quality, and ensure that data management processes are in tight alignment with our strategic analytics and decision-making objectives.
In this role, automating data pipelines and implementing scalable solutions will be just the beginning. You will also ensure data availability and integrity through effective governance, advocate for DataOps methodologies alongside IT and data teams, and continuously monitor, troubleshoot, and optimize data systems for superior performance.
Skillset:
- Advanced proficiency in database technologies such as SQL Server, Oracle, MySQL, or PostgreSQL for data management and querying.
- Expertise in implementing and managing data pipelines.
- Strong understanding of data warehousing concepts, data modelling techniques, and schema design for building and maintaining data warehouses or data lakes.
- Proficiency in cloud platforms such as AWS, Azure, or Google Cloud for deploying and managing scalable data infrastructure and services.
- Knowledge of DevOps principles and practices for automating infrastructure provisioning, configuration management, and continuous integration/continuous deployment (CI/CD) pipelines.
- Strong scripting and programming skills in languages like Python, Bash, or PowerShell for automation, data manipulation, and orchestration tasks.
- Ability to collaborate with cross-functional teams including data engineers, data scientists, and business stakeholders to understand requirements, design data solutions, and deliver projects.
- Excellent communication skills to effectively convey technical concepts to non-technical stakeholders and collaborate with team members.
- Strong problem-solving skills to troubleshoot data issues, optimize performance, and improve reliability of data pipelines and infrastructure.
- Ability to stay updated with emerging technologies, trends, and best practices in the field of DataOps and data engineering.
- Initiative and drive to continuously improve skills, automate repetitive tasks, and streamline data operations processes for increased efficiency and productivity.
Data Operations Engineer employer: Peregrine
Contact Detail:
Peregrine Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Operations Engineer
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as SQL Server, AWS, and Python. Having hands-on experience or projects that showcase your skills with these technologies can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the DataOps field through platforms like LinkedIn. Engaging with current employees at StudySmarter or attending relevant meetups can provide insights into the company culture and expectations, which can be beneficial during interviews.
✨Tip Number 3
Prepare to discuss real-world scenarios where you've implemented data pipelines or automated processes. Being able to articulate your problem-solving approach and the impact of your solutions will demonstrate your practical experience and understanding of the role.
✨Tip Number 4
Stay updated on the latest trends in DataOps and data engineering. Mentioning recent developments or best practices during your conversations can show your enthusiasm for the field and your commitment to continuous learning, which is highly valued in our team.
We think you need these skills to ace Data Operations Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the DataOps Engineer role. Emphasise your proficiency in programming languages like Python or Scala, and your experience with database technologies and cloud platforms.
Craft a Compelling Cover Letter: Write a cover letter that showcases your understanding of DataOps principles and your ability to collaborate with cross-functional teams. Use specific examples from your past experiences to demonstrate how you have successfully implemented data solutions.
Showcase Your Technical Skills: In your application, clearly outline your technical skills related to data management, pipeline implementation, and DevOps practices. Mention any relevant projects or achievements that illustrate your expertise in these areas.
Highlight Problem-Solving Abilities: Employ examples in your application that showcase your strong problem-solving skills. Discuss how you've tackled data issues or optimised performance in previous roles, as this is crucial for the DataOps Engineer position.
How to prepare for a job interview at Peregrine
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in programming languages like Python or Scala, as well as your experience with database technologies such as SQL Server or PostgreSQL. Bring examples of past projects where you implemented data pipelines or automated solutions.
✨Demonstrate Collaboration Experience
Since collaboration is key in this role, be ready to share specific instances where you've worked with cross-functional teams. Highlight how you gathered requirements and crafted data solutions that met the needs of various stakeholders.
✨Understand DataOps Principles
Familiarise yourself with DataOps methodologies and be prepared to discuss how you've applied these principles in previous roles. Emphasise your ability to eliminate bottlenecks and improve data quality through effective governance.
✨Prepare for Problem-Solving Scenarios
Expect to face technical questions or scenarios that test your problem-solving skills. Think of examples where you've troubleshot data issues or optimised performance in data systems, and be ready to explain your thought process.
