Job Overview
Location
Germany
Job Type
Full-time
Category
Data Engineer
Date Posted
March 21, 2026
Full Job Description
đź“‹ Description
- • As a Full-Stack Data Platform Engineer at Reflow, you will design and operate the core data infrastructure that transforms complex workforce and workflow data into actionable intelligence, directly enabling analytics, economic modeling, and product insights that help teams understand how work gets done.
- • You will architect and maintain a scalable data warehouse using platforms like BigQuery, Snowflake, or Redshift, ensuring it supports flexible analytics, real-time reporting, and high-performance querying across growing data volumes.
- • You will develop, optimize, and monitor ETL pipelines to ingest data from diverse sources including APIs and internal systems, focusing on reliability, cost efficiency, and seamless integration with downstream analytics and product features.
- • You will model and manage datasets to support evolving analytical needs, applying strong data modeling principles to create clean, well-documented, and reusable data structures that power dashboards, metrics, and predictive insights.
- • You will collaborate closely with engineering, product, and analytics teams to improve data mining performance, implement event-based pipelines for real-time insights, and ensure data quality, governance, privacy, and security best practices are embedded in all systems.
- • You will build and maintain internal and external-facing dashboards and visualization tools using Metabase, Tableau, or Power BI, enabling stakeholders to explore data intuitively and derive meaningful insights without technical barriers.
- • You will continuously optimize data platform performance and cost, balancing scalability, complexity, and operational efficiency while advocating for best practices in cloud-based data operations and infrastructure as code.
- • You will contribute to a culture of data literacy and trust by ensuring data is accurate, accessible, and secure, helping Reflow scale its intelligence layer as the platform grows in sophistication and user base.
- • You will work directly with founders and cross-functional engineers in a high-impact, builder-oriented environment where technical excellence meets real-world product impact, shaping how data drives decisions across the organization.
- • You will gain deep expertise in modern data platform architecture, from ingestion to insight, while developing strategic thinking around how data infrastructure enables business strategy, automation, and innovation at scale.
🎯 Requirements
- • Proven experience designing, implementing, and maintaining scalable data warehouses (e.g., BigQuery, Snowflake, Redshift) and analytics infrastructure.
- • Strong proficiency in SQL, data modeling, and ETL pipeline development with hands-on experience in tools like Airflow, dbt, or similar frameworks.
- • Demonstrated ability to optimize data pipeline performance, manage costs, and ensure reliability and security in cloud-based data operations.
🏖️ Benefits
- • Competitive, market-based compensation tailored to location and experience, with transparent salary ranges covering both U.S. and international candidates.
- • Opportunity to work directly with founders and engineering leaders on high-impact technical challenges at the intersection of data, product, and strategy.
- • Flexible work arrangements including part-time or full-time options, ideal for builders who value precision, performance, and autonomy in a scaling startup environment.
Skills & Technologies
About Reflow
Reflow is a cloud-based platform designed to automate and streamline the process of software updates for IoT devices. It provides a secure and reliable solution for managing firmware over-the-air (FOTA) deployments, enabling companies to efficiently deliver updates, patches, and new features to their connected products. The platform offers features such as staged rollouts, rollback capabilities, and detailed monitoring to ensure smooth and successful updates. Reflow aims to reduce the complexity and risk associated with device management, allowing manufacturers to maintain product security, enhance user experience, and extend the lifespan of their IoT ecosystems without manual intervention.
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