As a Junior Analytics Engineer within the EMEA Business Intelligence team, you will be an integral part of the Analytics Engineering pillar, contributing to the development and maintenance of the data foundation that powers BI delivery across EMEA. Working closely with senior Analytics Engineers, you will support the design and build of semantic models, curated datasets, and reusable data assets on our central data platform, gaining hands-on experience with enterprise-scale data modelling and platform engineering in a collaborative, high-performing team. You will work in close collaboration with the BI and Data Science & Engineering teams, helping to lay the groundwork for data assets that increasingly support AI and agentic use cases alongside traditional BI delivery. You will start to develop an understanding of the business context behind the data you work with, helping to translate business needs into well-structured data assets and beginning to build the analytics translation skills that connect data and business outcomes. This is a role for someone who is technically curious, eager to learn, and motivated to build a strong foundation in analytics engineering. You will be guided and supported throughout, with clear opportunities to grow your skills, take on increasing responsibility, and develop toward a more senior role within the pillar.
Responsibilities:
- Contribute to the design, build, and maintenance of semantic models and curated datasets on our central data platform, working under the guidance of senior Analytics Engineers.
- Support the standardisation of datasets, business logic, and data models to enable reusability and consistency across BI teams and markets.
- Assist with onboarding market-specific datasets onto our central data platform, collaborating with local stakeholders to understand data sources and requirements.
- Work closely with senior team members to ensure data assets meet the requirements of BI products being developed by the BI Development team.
- Develop a working understanding of the business context behind data requests, supporting senior team members in translating business needs into data structures and logic.
- Collaborate closely with Data Science & Engineering colleagues, building familiarity with how data assets are used and shared across teams.
- Contribute to data quality checks and basic performance monitoring of data models and semantic layers, supporting senior team members in identifying improvements.
- Support early-stage work on building data foundations that can be used for AI and agentic use cases, such as preparing clean, well-structured datasets ready for future AI applications.
- Maintain clear documentation of data models, datasets, and platform assets, ensuring work is well-described and easy for others to understand and build on.
- Participate in peer reviews of data models and platform assets, incorporating feedback constructively to develop technical skills and maintain quality standards.
- Follow established best practices for data modelling, naming conventions, and platform governance, developing familiarity with the team's standards and ways of working.
- Engage actively in training, bootcamps, and mentoring programmes, taking ownership of personal development and building expertise in the tools and technologies used by the team.
- Support testing and quality assurance activities across project delivery, helping to ensure data assets and platform changes are reliable before release.
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Engineering, Mathematics, Statistics, or a related technical discipline.
- Some practical experience with data engineering, analytics engineering, or BI development through employment, internships, or academic projects.
- Foundational understanding of SQL and a willingness to develop proficiency in building and optimising data models.
- Basic familiarity with data modelling concepts such as star schema or dimensional modelling; formal experience is an advantage but not required.
- Exposure to enterprise data platforms such as Databricks, Microsoft Fabric, Azure Synapse, or Snowflake is desirable.
- Awareness of Power BI or other BI tools and an interest in understanding how data assets underpin BI product development.
- Curiosity about AI, machine learning, and agentic technologies, and how data platforms are structured to support these use cases.
- An interest in understanding business needs and translating them into data and analytics solutions, with a willingness to develop commercial awareness alongside technical skills.
- Strong attention to detail and a methodical approach to building, testing, and documenting data assets.
- A collaborative and proactive mindset, with the eagerness to learn from senior team members and contribute to a high-performing team.
- Good communication skills, with the ability to ask clear questions and document work clearly.
- Knowledge of the real estate sector is a plus but not required.
- Fluent in English; additional European languages are an advantage.
