-
Job Description
Are you ready to make a significant impact in the world of travel disruption? Join us as a Data Engineer in Manchester, where you'll play a crucial role in our fast-growing team at the forefront of innovation.
Why Swiipr?
At Swiipr, we're not just transforming travel disruption - we're redefining what it means to deliver exceptional customer experiences in a rapidly evolving world. Our innovative fintech solutions and cutting-edge technology empower airlines to navigate disruptions with ease, putting customer satisfaction and digital efficiency at the forefront of their operations.
Your Role
As a Data Engineer, you'll be at the forefront of our data strategy, using your expertise in Azure Data Factory, SQL Server, and Power BI to shape our data infrastructure. From designing and implementing data pipelines to ensuring our systems are optimized for performance and reliability, you'll play a vital role in unlocking the power of our data.
But it's not just about the technical side of things. You'll also have the opportunity to collaborate closely with our team, translating business requirements into technical solutions and ensuring our data solutions align with our clients' needs.
Key Responsibilities
Proficiency in Azure Data Factory: As a Data Engineer, you should have a strong command of Azure Data Factory, including its various components and functionalities. Experience with building and managing data pipelines in Azure Data Factory is essential.
Expertise in SQL Server: A solid understanding of SQL Server is crucial for this role. You should be proficient in writing complex SQL queries, optimizing database performance, and managing databases effectively.
Visual Reporting with Power BI: Experience with Power BI is a must. You should be able to create visually appealing and interactive reports and dashboards to communicate insights effectively to stakeholders.
Data Modelling: A fundamental understanding of data modelling concepts is essential for designing efficient and scalable data solutions. You should be able to design and implement data models that support the organisation's business requirements.
ETL Development: Experience with Extract, Transform, Load (ETL) development is necessary for building and maintaining data pipelines. You should be familiar with ETL tools and techniques for data integration and transformation.
Problem-Solving Skills: As a Data Engineer, you'll encounter various data-related challenges. Strong problem-solving skills are essential for identifying issues, troubleshooting problems, and implementing effective solutions.
Communication Skills: Effective communication is key, as you'll be collaborating with cross-functional teams and interacting with clients. You should be able to communicate technical concepts to non-technical stakeholders and work collaboratively in a team environment.
Analytical Thinking: An analytical mindset is crucial for interpreting data, identifying trends, and extracting actionable insights. You should be able to analyse complex data sets and draw meaningful conclusions to support decision-making.
Continuous Learning: The field of data engineering is constantly evolving, so a willingness to learn and adapt to new technologies and best practices is essential. Staying updated on the latest trends and advancements in data engineering is crucial for success in this role.
Attention to Detail: Data accuracy and integrity are paramount in data engineering. Attention to detail is crucial for ensuring data quality and reliability throughout the data lifecycle.
Qualifications & Experience
#J-18808-Ljbffr
Why Swiipr?
At Swiipr, we're not just transforming travel disruption - we're redefining what it means to deliver exceptional customer experiences in a rapidly evolving world. Our innovative fintech solutions and cutting-edge technology empower airlines to navigate disruptions with ease, putting customer satisfaction and digital efficiency at the forefront of their operations.
Your Role
As a Data Engineer, you'll be at the forefront of our data strategy, using your expertise in Azure Data Factory, SQL Server, and Power BI to shape our data infrastructure. From designing and implementing data pipelines to ensuring our systems are optimized for performance and reliability, you'll play a vital role in unlocking the power of our data.
But it's not just about the technical side of things. You'll also have the opportunity to collaborate closely with our team, translating business requirements into technical solutions and ensuring our data solutions align with our clients' needs.
Key Responsibilities
- Collaborate with the team to develop and implement data solutions using Azure Data Factory, SQL Server, and Power BI.
- Design, develop, and maintain data models and ETL pipelines to support business requirements.
- Translate business requirements into technical specifications and contribute to the overall architecture of data solutions.
- Work closely with senior team members to share knowledge and best practices, fostering a culture of continuous learning.
- Participate in client interactions, providing expertise and guidance on data engineering solutions.
- Stay updated on the latest trends and advancements in Azure technologies, proactively seeking opportunities for innovation.
- Adhere to industry standards and best practices in data governance, security, and compliance.
Proficiency in Azure Data Factory: As a Data Engineer, you should have a strong command of Azure Data Factory, including its various components and functionalities. Experience with building and managing data pipelines in Azure Data Factory is essential.
Expertise in SQL Server: A solid understanding of SQL Server is crucial for this role. You should be proficient in writing complex SQL queries, optimizing database performance, and managing databases effectively.
Visual Reporting with Power BI: Experience with Power BI is a must. You should be able to create visually appealing and interactive reports and dashboards to communicate insights effectively to stakeholders.
Data Modelling: A fundamental understanding of data modelling concepts is essential for designing efficient and scalable data solutions. You should be able to design and implement data models that support the organisation's business requirements.
ETL Development: Experience with Extract, Transform, Load (ETL) development is necessary for building and maintaining data pipelines. You should be familiar with ETL tools and techniques for data integration and transformation.
Problem-Solving Skills: As a Data Engineer, you'll encounter various data-related challenges. Strong problem-solving skills are essential for identifying issues, troubleshooting problems, and implementing effective solutions.
Communication Skills: Effective communication is key, as you'll be collaborating with cross-functional teams and interacting with clients. You should be able to communicate technical concepts to non-technical stakeholders and work collaboratively in a team environment.
Analytical Thinking: An analytical mindset is crucial for interpreting data, identifying trends, and extracting actionable insights. You should be able to analyse complex data sets and draw meaningful conclusions to support decision-making.
Continuous Learning: The field of data engineering is constantly evolving, so a willingness to learn and adapt to new technologies and best practices is essential. Staying updated on the latest trends and advancements in data engineering is crucial for success in this role.
Attention to Detail: Data accuracy and integrity are paramount in data engineering. Attention to detail is crucial for ensuring data quality and reliability throughout the data lifecycle.
Qualifications & Experience
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- Minimum of 5 years of industry experience in implementing data solutions using Azure Data Factory, SQL Server, and Power BI.
- Solid understanding of data modelling, ETL development, and data warehousing principles.
- Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues.
- Proven track record of delivering high-quality data solutions that meet business requirements.
- Familiarity with Agile methodologies and experience working in Agile development teams is preferred.