Founding Data Engineer (Analytics Platform)

Das ist der Job

This position is listed on behalf of a partner company, who manages all applications and next steps.

Darum lohnt es sich

This is a unique opportunity to join an ambitious technology team at an early stage and build the foundation of a modern data ecosystem from the ground up. Working closely with leadership and cross-functional teams, you will shape architecture decisions, engineering standards, and data practices.

You will work with modern cloud technologies and build reliable pipelines that transform complex data into actionable insights.

Accountabilities: The Founding Data Engineer will lead the design and development of the organization’s analytics platform, establishing scalable data infrastructure and ensuring reliable access to high-quality information across teams.

Collaborate with Product, Growth, and Engineering teams on product analytics, experimentation, reporting, event tracking, and API integrations. Hands-on experience with modern cloud data warehouses such as Snowflake, BigQuery, or Redshift. Benefits: Competitive compensation package.

Opportunity to build and shape a modern data platform from an early stage. Access to contemporary technologies and a modern engineering environment. The final decision and next steps (interviews, assessments) are managed by their internal team. These tools assist our recruitment team but do not replace human judgment.

Our partner is looking for a Founding Data Engineer (Analytics Platform) based in Germany. You will take ownership of designing and scaling the data platform that powers analytics, business intelligence, and future AI initiatives.

The role combines hands-on technical execution with strategic influence, giving you the opportunity to create systems with long-term impact. This position is ideal for an engineer who enjoys ownership, innovation, and building high-quality solutions in a fast-moving environment.

Design, build, and maintain scalable ELT/ETL pipelines that integrate product data, payment systems, and third-party APIs. Architect and manage a cloud-based data warehouse environment using technologies such as Snowflake, BigQuery, or Redshift.

Develop reliable data orchestration workflows using tools such as Airflow, Prefect, Dagster, or similar platforms. Optimize data warehouse performance, scalability, and cost through effective modeling and query optimization. Create clean, testable transformation workflows using dbt or equivalent frameworks.

Establish data quality processes, including testing, monitoring, lineage tracking, and documentation. Build secure and privacy-conscious data practices, including access controls and appropriate handling of sensitive information. Design semantic layers and consistent business metrics to support analytics and decision-making across the organization.

Promote DataOps best practices through CI/CD, version control, automated testing, and documentation standards. Contribute to the evolution of the data strategy and establish technical foundations for future AI and machine learning initiatives.

Requirements: The ideal candidate is a proactive Data Engineer with strong technical expertise, a builder mindset, and experience creating reliable production-grade data systems. 3+ years of experience as a Data Engineer or in a similar data-focused engineering role.

Strong SQL skills and advanced proficiency in Python; experience with Scala is a plus. Experience designing and maintaining production data pipelines and scalable data architectures. Practical knowledge of workflow orchestration tools such as Airflow, Prefect, Dagster, or similar technologies.

Strong understanding of data modeling principles and experience with dbt or comparable transformation frameworks. Experience implementing data quality checks, monitoring, governance, and documentation practices. Ability to communicate technical concepts clearly and collaborate effectively with product and engineering stakeholders.

Strong ownership mindset with a focus on reliability, scalability, and long-term maintainability. Experience applying secure and privacy-aware data practices. Nice-to-have qualifications include: Experience working in B2C SaaS, subscription-based, or marketplace environments.

Familiarity with product analytics platforms such as Segment, Amplitude, Mixpanel, or similar tools. Experience designing semantic layers or canonical data models. Exposure to streaming technologies such as Kafka or Kinesis. Experience with ML infrastructure, feature stores, or AI-related data systems.

Previous experience building data platforms in startup or scale-up environments. Fully remote work setup with flexible working hours. 22 paid vacation days plus local public holidays. Significant ownership and influence over architecture decisions and engineering practices.

Opportunity to solve meaningful technical challenges with direct business impact. Collaborative, product-focused culture where data plays a central role in decision-making. Professional growth opportunities within a rapidly evolving technology environment.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. We appreciate your interest and wish you the best!

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