Data Engineer (mfd)

Cyber Insight GmbH; Leipzig; Hybrid; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A dynamic cybersecurity startup in Leipzig is looking for a Data Engineer to design and maintain reliable data systems for AI-driven risk management. Candidates should have at least 3 years of experience in data engineering with strong Python skills. The role offers a collaborative environment and flexible working hours.

Leistungen

Flexible working hours

Remote-friendly setup

Competitive salary and tailored benefits

Exposure to cutting-edge technologies

Qualifikationen

• 3 years of experience in data engineering or cybersecurity data processing.
• Experience integrating heterogeneous data sources.
• Knowledge of security-focused data handling practices.

Aufgaben

• Design, build, and maintain data pipelines across GCP and on-prem environments.
• Ingest and process cybersecurity-relevant data sources.
• Develop data models linking vulnerabilities to software and assets.
• Collaborate with AI teams to prepare data for risk models.

Kenntnisse

Strong Python skills

Experience with pandas

Proven experience with data orchestration frameworks

Solid understanding of data modeling

Familiarity with CVE data structures

Knowledge of GCP data tools

Experience with containerized environments

Understanding of data testing and validation

Tools

Airflow

Docker

PostgreSQL

BigQuery

Jobbeschreibung

At Cyber Insight we are building the next generation of AI-driven platforms for IT security and risk management. Our mission is to empower companies to gain deep insights into their IT landscapes and proactively mitigate risks in an increasingly complex digital world.

As a fast-growing startup we combine expertise in cybersecurity data engineering and artificial intelligence to deliver solutions that automate risk assessments predict potential threats and help organizations stay ahead of evolving cyber risks. Our team thrives on innovation collaboration and a shared passion for making a real impact in the cybersecurity space.

We are looking for a hands-on Data Engineer who is passionate about building reliable scalable and secure data systems. Youll help shape our data architecture and pipelines that feed our AI models and risk assessment engines including the crucial task of mapping vulnerabilities (CVEs) to specific software and system components.

Tasks

• Design build and maintain data pipelines and ETL / ELT workflows across GCP and on-prem environments.
• Ingest and process cybersecurity-relevant data sources such as CVE feeds software inventories vulnerability databases and event logs.
• Develop and maintain transformation logic and data models linking vulnerabilities (CVEs) to affected software and assets.
• Implement and automate data validation consistency checks and quality assurance using tools like Great Expectations or Deequ .
• Collaborate with AI and graph modeling teams to structure and prepare data for threat intelligence and risk quantification models .
• Manage and optimize data storage using BigQuery PostgreSQL and Cloud Storage ensuring scalability and performance.
• Automate data workflows and testing through CI / CD pipelines (GitHub Actions GCP Cloud Build Jenkins).
• Implement monitoring and observability for pipelines using Prometheus Grafana and OpenTelemetry .
• Apply a security-focused mindset in data handling ensuring safe ingestion processing and access control of sensitive datasets.

Requirements

3 years of experience in data engineering backend data systems or cybersecurity data processing .

• Strong Python skills and experience with pandas PySpark or Dask for large-scale data manipulation.
• Proven experience with data orchestration and transformation frameworks (Airflow dbt or Dagster).
• Solid understanding of data modeling data warehousing and SQL optimization and ETL pipelines (Kafka) .
• Familiarity with CVE data structures vulnerability databases (e.g. NVD CPE CWE) or security telemetry.
• Experience integrating heterogeneous data sources (APIs CSV JSON XML or event streams).
• Knowledge of GCP data tools (BigQuery Pub / Sub Dataflow Cloud Functions) or equivalent in Azure / AWS.
• Experience with containerized environments (Docker Kubernetes) and infrastructure automation (Terraform or Pulumi).
• Understanding of data testing validation and observability practices in production pipelines.
• A structured and security-aware approach to building data products that support AI-driven risk analysis .

Nice to Have

• Experience working with graph databases (Neo4j ArangoDB) or ontology-based data modeling .
• Familiarity with ML pipelines (Vertex AI Pipelines MLflow or Kubeflow).
• Understanding of software composition analysis (SCA) or vulnerability scanning outputs (e.g. Trivy Syft).
• Background in threat intelligence risk scoring or cyber risk quantification .
• Experience in multi-cloud or hybrid setups (GCP Azure on-prem).

Benefits

• Freedom to design and shape a modern secure data platform from the ground up.
• A collaborative startup environment where your work directly supports AI and cybersecurity products.
• Flexible working hours and remote-friendly setup.
• Exposure to cutting-edge technologies in AI data engineering and cyber risk analytics .
• Competitive salary and benefits tailored to your experience.

Key Skills

Laboratory Experience,Vendor Management,Design Controls,C / C++,FDA Regulations,Intellectual Property Law,ISO 13485,Research Experience,SolidWorks,Research & Development,Internet Of Things,Product Development

We are looking forward to meet you!

Employment Type : Employee

Experience : years

Vacancy : 1