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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 like AWS or Azure is essential.
- Other info: Ideal for tech-savvy individuals eager to make an impact 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 organisation's data science and analytics ambitions.
Collaboration is a cornerstone of this position. You will work closely with teams across the organisation, 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 optimise data systems for superior performance.
- 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, optimise 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.
DataOps Engineer employer: Peregrine
Contact Detail:
Peregrine Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land DataOps 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 industry groups or attending relevant meetups can help you gain insights into the role and potentially connect you with someone at StudySmarter.
✨Tip Number 3
Prepare to discuss your experience with data pipelines and automation during interviews. Be ready to share specific examples of how you've implemented solutions that improved data quality or streamlined processes in previous roles.
✨Tip Number 4
Showcase your collaborative skills by highlighting any past experiences where you worked closely with cross-functional teams. Emphasising your ability to communicate technical concepts to non-technical stakeholders will demonstrate your fit for the role.
We think you need these skills to ace DataOps 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. Mention specific projects where you've implemented automated solutions or optimised data pipelines.
Showcase Your Technical Skills: In your application, include examples of your work with data warehousing concepts, schema design, and DevOps practices. Highlight any experience you have with CI/CD pipelines and how you've used them to improve data management processes.
Prepare for Technical Questions: Anticipate technical questions related to data integration, quality control, and troubleshooting data issues. Be ready to discuss your problem-solving approach and how you stay updated with emerging technologies in DataOps.
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. Highlight specific projects where you've implemented data pipelines or automated solutions.
✨Demonstrate Collaboration Abilities
Since collaboration is key in this role, be ready to share examples of how you've worked with cross-functional teams. Discuss how you gathered requirements from stakeholders and crafted data solutions that met their needs.
✨Discuss DataOps Best Practices
Familiarise yourself with DataOps methodologies and be prepared to talk about how you've implemented best practices in your previous roles. Explain how these practices helped eliminate bottlenecks and improve data quality.
✨Prepare for Problem-Solving Scenarios
Expect to face questions that assess your problem-solving skills. Think of specific instances where you troubleshot data issues or optimised performance in data systems, and be ready to explain your thought process.
