Analytics Engineer, MILES
Car sharing and subscription
Be an early applicant
Car sharing and subscription
201-500 employees
Be an early applicant
201-500 employees
Company mission
To reduce the number of vehicles on the road and improve urban life.
Role
Who you are
- Education: You hold a Master’s degree in a relevant field such as Data Engineering, Data Science, Computer Science, Business Informatics, or a related discipline
- Experience: 2-3 years of relevant experience in Data Engineering, Analytics Engineering or a related field. Ideally in a fast-paced environment such as a Start-Up or Scale-Up
- Profile: You are detail-oriented and deeply analytical, with a strong drive to create robust and scalable data systems. You thrive on optimizing workflows, automating repetitive tasks, and building high-quality data pipelines that support downstream analytics. You take ownership, write clean and modular code, and care deeply about data quality, maintainability, and documentation
- Advanced SQL skills (preferred Snowflake). Comfortable writing, maintaining and improving complex queries, joins, aggregations, and data transformations
- You have a good understanding of dimensional modeling and star/snowflake schema design. Experience building semantic layers (dbt Metric Layer) is a plus
- Proficiency with Python, Airflow and dbt, with the ability to optimize materialization strategies for cost and performance
- Familiarity with Git-based workflows
- While not your primary focus, experience with a data visualisation tool such as Tableau is a plus
- AI Exposure: Strong interest in AI technologies and their practical application in business processes. Previous experience with AI tools is a plus
- Languages: Excellent communication skills in English (both written and verbal). Proficiency in German is a significant advantage
What the job involves
- As a Analytics Engineer at MILES you will be responsible for building and maintaining the foundational data infrastructure that powers analytics across the organization.
- You will work at the intersection of data engineering and data analysis, transforming raw data into clean, well-modeled datasets that are trusted, easy to use, and optimized for business decision-making.
- Partnering closely with cross-functional stakeholders, you will own the development and maintenance of scalable data models, support data governance initiatives, and ensure data quality throughout the pipeline.
- Your work will be key to enabling data self-service, supporting advanced analytics, and fostering a culture of data-driven thinking across teams
- End-to-End Ownership of Data Modeling & Pipelines: Own and manage the full lifecycle of analytics engineering projects, from data ingestion and transformation to production-ready, scalable data models. You will ensure alignment with analytics use cases by building clean, well-documented models that serve as the foundation for reporting
- Data Transformation & Quality Assurance: Design, implement, and optimize SQL-based dbt models to support analytics across the organization. Ensure high standards of data quality by proactively monitoring pipelines, validating metrics, and resolving anomalies. Contribute to a culture of data reliability through automated tests, CI/CD practices, and clearly documented KPIs
- Scalable & Maintainable Data Infrastructure: Build and maintain robust ELT/ETL pipelines that scale with data volume and complexity. Use a combination of dbt, SQL, and Python-based tools to create efficient pipelines. Continuously improve performance and maintain production stability through monitoring and observability
- Documentation & Data Governance: Ensure data models, metrics definitions, and business logic are well-documented and version-controlled. Support data governance by promoting best practices in naming conventions, lineage tracking, and semantic consistency
- AI & Tooling Enablement: Support company-wide adoption of AI and automation by enabling access to clean, AI-ready data. Enjoy the freedom to experiment, explore and build small applications of AI and RAG. Contribute to internal initiatives that educate teams on AI tooling
Our take
Car-sharing provides a convenient and cost-effective alternative to car ownership, particularly in large cities where congestion is high and parking space is at a premium. Founded in 2016, MILES has made it easy for German and Belgian commuters to get around without owning a car or getting on a bus.
The German ride sharing sector is quite heavily contested, and while MILES is one of the larger providers it lacks the customer base of its primary rival SHARE NOW. Where it hopes to compete is in its pricing plan, which is measured by distance rather than minutes, providing a less pressured experience for its customers. So far this has worked well for the company, allowing it to multiply its revenue year on year and achieve profitability only four years after its founding. It currently serves more than 200,000 customers and has a fleet of over 13,000 vehicles.
MILES acquired the car sharing arm of Volkswagen in 2022 and integrated it into its own offering. Aside from increasing the size of MILES' fleet part of the deal allows VW customers to book cars through their existing VW mobility app, increasing MILES' customer base. MILES' is working to increase the electrification of its fleet, expand its market presence in Germany and Belgium, and develop new products for its customers such as longer-term rentals and city-to-city rides.
Freddie
Company Specialist at Welcome to the Jungle
Insights
62% employee growth in 12 months
Company
Funding (1 round)
Mar 2019
$5.8m
SERIES A
Total funding: $5.8m
Company benefits
- Work from home available
- MILES goodies
- You can quickly take on a lot of responsibility, contribute your ideas and enjoy the openness of all colleagues
Company HQ
Charlottenburg, Berlin, Germany
Leadership
Alexander Eitner
(COO)Was previously Founder & COO of spotcar and the owner of a car rental shop.
Salary benchmarks
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