Data Engineer
Job descriptionAre you passionate about building powerful data solutions that drive real business impact? STADIO is looking for a skilled and motivated Mid-Level Data Engineer to join our dynamic Continuous Improvement & Innovation team. In this key role, you’ll help shape the backbone of our data infrastructure—designing and maintaining robust data pipelines and aggregation layers that empower data-driven decision making across the organisation. If you thrive in a collaborative environment, believe in the power of data to drive improvement initiatives and are excited about working with cutting-edge technologies we’d love to hear from you.About the Role STADIO is seeking a Mid-Level Data Engineer to join our dynamic Continuous Improvement and Innovation team. In this role, you will play a key part in our continuous improvement lifecycle by developing the data infrastructure that powers data-driven decision making across the organisation. You will work closely with our Analytics Engineer to build and maintain robust data pipelines, a scalable data aggregation layer, and an efficient reporting environment. This is an exciting opportunity for a data engineering professional with a passion for large datasets and modern cloud technologies to make a real impact in a collaborative, forward-thinking environment. Key Responsibilities
Qualifications and Skills Required Education and Experience o Bachelor’s degree in Engineering, Information Systems, Computer Science, or a related field. o 4+ years of hands-on experience in data engineering or a similar role, with a track record of working on large-scale datasets and building data aggregation layers. Technical Skills: o Modern Data Stack Proficiency: Practical experience building data pipelines in a modern cloud environment. Familiarity with data lake architectures, SQL databases, and data integration tools on Azure (or similar platforms) is required. o SQL and Programming Skills: Advanced SQL skills for data querying and transformation. Proficiency in at least one programming or scripting language (e.g., Python, .NET) for data processing. o Data Modeling Knowledge: Solid understanding of data modeling techniques and designing scalable schema for analytics. o Performance Tuning: Knowledge of optimising database and data pipeline performance (indexing, partitioning, caching strategies). o Expertise in Microsoft Data Technologies: Strong experience with Microsoft’s data stack, especially Microsoft Fabric and its underlying components. Proficiency in tools such as Azure Data Factory (for ETL/ELT pipelines) and Azure Synapse Analytics (for data warehousing and big data processing) will be an advantage. o Additional Cloud and Big Data Tools: Exposure to other cloud data services and tools (such as Azure Databricks, Azure Data Lake Storage, Power BI, or comparable tools on AWS/GCP) will be an advantage. o Automation and Orchestration: Experience with workflow orchestration tools and CI/CD pipelines for data (e.g., Azure Data Factory pipelines, Git integration, DevOps for data processes). Preferred Skills and Attributes: o Analytical Mindset: Strong problem-solving skills and the ability to translate business requirements into efficient data solutions. Attention to detail in ensuring data accuracy and integrity. o Communication and Teamwork: Excellent communication skills with the ability to work effectively in a collaborative team environment. Able to explain complex data concepts to non-technical stakeholders when needed. o Agile Methodology: Comfortable working in Agile/Scrum teams and using tools for ticketing and collaboration (Azure DevOps, JIRA, etc.). o Continuous Learner: Enthusiasm for staying up-to-date with emerging data technologies and best practices. A proactive attitude towards learning and continuous improvement will fit well with our culture. Why Join Us?
What We Offer
Posted on 05 Jan 09:38, Closing date 18 Jan Or apply with your Biz CVCreate your CV once, and thereafter you can apply to this ad and future job ads easily. See also: Engineer, Software Engineer | ||||||||||||