Your Role

At emnify, we are scaling our IoT connectivity platform and strengthening our data capabilities to power internal decision-making and customer-facing insights. We are looking for a Staff Data Engineer to design, build, and evolve our data platform to support growing data volumes, real-time use cases, advanced analytics, and AI-driven capabilities.

In this senior, high-impact role, you will work closely with product, engineering, analytics, and data teams to ensure our data infrastructure is scalable, reliable, and future-proof. You will shape architectural decisions, define engineering standards, and remain hands-on in building robust data systems.

Our flexible work model includes monthly in-person workshops. Candidates based in Berlin or nearby cities are preferred.

Location: Berlin, Germany (or remote within the EU, with preference for proximity to Berlin)

Your Impact

• Design and scale the data platform

Lead the architecture and implementation of batch and real-time data pipelines that support analytics, operational use cases, and customer-facing data products.

• Enable real-time and customer-facing use cases

Build low-latency data workflows and scalable data access layers to power embedded analytics, customer-facing dashboards, and analytics APIs consumed by internal systems and external customers.

• Ensure reliability and data quality

Drive best practices in data modeling, testing, monitoring, governance, and observability to improve trust and usability across the organization.

• Support AI and advanced analytics workflows

Build and maintain the infrastructure enabling scalable AI use cases, including feature pipelines, model deployment (batch and real-time), and monitoring of model performance and data quality.

• Raise the engineering bar

Act as a technical leader within the data team: define standards, mentor peers, review architecture, and continuously improve our tooling and processes.

Your Skills

• Strong data engineering & lakehouse expertise

Proven experience designing and operating scalable data architectures, including modern lakehouse architectures (e.g., object storage + table formats + compute engines).

You have hands-on experience building reliable batch and streaming pipelines on top of a lakehouse foundation.

• Real-time data processing

Hands-on experience with streaming technologies such as Kafka, Flink, or Spark Streaming, and high-performance analytical databases such as Apache Druid, ClickHouse, or StarRocks.

• Cloud and infrastructure engineering

Strong experience building and operating data platforms in cloud environments (AWS mandatory).

Deep hands-on knowledge of infrastructure-as-code (e.g., Terraform), container orchestration (e.g., Kubernetes, EKS), and CI/CD practices.

You are comfortable with self-hosting and operating data systems across multiple environments, particularly in contexts with high data growth and strict reliability requirements.

• Data modeling & multi-tenant systems

Experience designing scalable data models and analytics architectures in multi-tenant SaaS environments.

• Software engineering mindset

Strong programming skills (SQL, Python, Scala, or Java) with emphasis on clean, maintainable, production-grade code.

• AI/ML platform exposure

Experience supporting ML/AI workflows in production environments, including model deployment pipelines, inference services, monitoring, and integration into real-time systems.