Senior Data Generation Engineer
Splunk
Key Responsibilities:
- Design and implement data generation frameworks to simulate traffic and security events under diverse scenarios that mirror real world environments.
- Collaborate with technical marketing teams to understand data requirements and ensure realistic data simulation.
- Develop and maintain documentation and methodologies.
- Analyze data & processes to improve efficiency and accuracy.
- Ensure data quality and relevance by continuously refining simulation parameters and models.
- Automate data generation tasks using appropriate tools and scripting languages.
- Conduct regular reviews of data simulation strategies and implement improvements based on feedback and new insights.
- Stay informed about emerging trends and technologies in data simulation and apply them to improve existing frameworks.
Qualifications:
- Bachelor’s degree in Computer Science, Data Science, Data Engineering, or a closely related technical subject area (or equivalent professional experience).
- Minimum of 5 years hands-on professional experience focused on data engineering, simulation, or similar fields involving synthetic data creation and management.
- Proven capability in crafting and implementing frameworks to generate realistic simulated data representing network traffic and cybersecurity events.
- Comprehensive understanding of data modeling techniques, generation methodologies, and analytical processes used to emulate real-world scenarios.
- Demonstrated proficiency in scripting and automation languages, such as Python, Bash, or similar scripting languages commonly used in data generation and pipeline automation.
- Familiarity with common data simulation tools, data streaming services, and technologies capable of generating synthetic network or system traffic at scale.
- Experience in validating simulated data to ensure accuracy, realism, and relevance for various testing and marketing use-cases.
- Strong interpersonal and teamwork skills enabling effective multi-functional collaboration.
- Proven analytical skills to evaluate data generation workflows, identify bottlenecks, and implement efficiency improvements.
- Experience with data generation tools and frameworks (e.g., Ostinato, Netropy, Attack Range)
- Familiarity with cloud platforms and big data technologies, including AI.
- Hands-on experience with Splunk solutions or similar industry platforms is a plus.
Note: