At a Glance
- Tasks: Design and develop innovative HR data solutions using Azure Databricks.
- Company: Join a forward-thinking company focused on quality and automation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative team culture with a focus on continuous learning and development.
- Why this job: Make a real impact by delivering high-quality data solutions in an agile environment.
- Qualifications: Experience in data engineering with Azure and strong coding skills in Python/Scala.
The predicted salary is between 63000 - 77000 £ per year.
We are looking for an experienced Azure Databricks Data Engineer. The role focuses on designing, configuring, and developing HR solutions across the employee lifecycle, with strong emphasis on quality, automation, and scalable design.
Responsibilities:
- 8-10 years of hands-on experience in data engineering space, with ability to translate working solution into implementable working package and/or build new solution using Azure platform.
- High proficiency and experience in designing/developing data analytics and data warehouse solutions with Azure Data Factory (ADF) and Azure Databricks, services related to Azure Analytics, Azure data lake storage Gen2, Azure SQL/Postgres, Azure analytics services and good understanding of Azure identity (SPN, SAMI, UAMI etc.).
- Experience in designing large data distribution, integration with service-oriented architecture, data mesh, data Lake and data analytics solution using Azure data services with large and multi-format data.
- Proficient coding experience using Spark (Python/Scala) and SQL.
- Hands-on experience with one of scripting language: Linux / PowerShell.
- Prior ETL development experience using industry tool e.g. Informatica/SSIS/Talend etc., good to have knowledge in Kafka streaming.
- A proven track record of work experience in cloud (MS Azure platform preferred) in an agile SDLC environment, leveraging modern programming languages, DevOps, and test-driven development.
- Exposure to Docker, Git.
- Hands-on experience with pipeline orchestration tools like Apache Airflow, Autosys, Control-M – Preferred Apache Airflow.
- Proficient at working with large and complex code bases using GitLab/Github, Fork/Pull Model and CI/CD.
- Knowledge of DevOps tools and technologies (like GitLab CI/CD pipeline creation, YAML, Terraform, ARM, Puppet), Azure function app, logic app is a plus.
- Working experience in Agile methodologies (SCRUM, XP, Kanban).
You should hold a relevant bachelor’s degree or equivalent. Excellent communication skills, with fluency in English for both presentations and technical writing.
Profile we are looking for:
A pragmatic, hands-on Azure Databricks Data Engineer who is comfortable working in an HR context, values automated testing and quality, and can independently deliver well-engineered HR solutions while collaborating closely with cross-functional teams.
Requirements:
- Design, develop, and improve the digital products and technology services we provide to our clients and employees.
- Apply a broad range of software engineering practices, from analyzing user needs and developing new features to automated testing and deployment.
- Ensure the quality, security, reliability, and compliance of our solutions by applying our digital principles and implementing both functional and non-functional requirements.
- Build observability into our solutions to monitor production health, help to resolve incidents, and remediate the root cause of risks and issues.
- Continuously up-skill, learn new technologies and practices, reuse strategic platforms and standards, evaluate options, and make decisions with long-term sustainability in mind.
Data Engineer, Azure Databricks employer: Colate
Colate is an exceptional employer that fosters a collaborative and innovative work culture, perfect for those looking to make a significant impact in the tech industry. With a focus on employee growth, we offer ample opportunities for professional development and skill enhancement, all while working remotely from the UK. Join us to be part of a forward-thinking team dedicated to building cutting-edge technology in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer, Azure Databricks
✨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 Colate!
✨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 Data Engineer, Azure Databricks at Colate.
✨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 Colate.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer, Azure Databricks at Colate, 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 Data Engineer, Azure Databricks
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 Colate, 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 Colate. 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 Colate
✨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 Colate!
✨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.
