As part of the cybersecurity organization, the Data Engineer is responsible for designing, building, and maintaining data infrastructure to support data-driven decision-making. This role involves working with large datasets, developing reports, executing data governance initiatives, and ensuring data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture, ETL processes, and cybersecurity data frameworks.
Roles & Responsibilities
- Design, develop, and maintain data solutions for data generation, collection, and processing.
- Be a key team member that assists in the design and development of the data pipeline.
- Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems.
- Schedule and manage workflows to ensure pipelines run on schedule and are monitored for failures.
- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs.
- Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency.
- Implement data security and privacy measures to protect sensitive data.
- Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions.
- Collaborate and communicate effectively with product teams.
- Collaborate with data scientists to develop pipelines that meet dynamic business needs.
- Share and discuss findings with team members practicing the SAFe Agile delivery model.
Functional Skills
Basic Qualifications:
- Master's degree and 1 to 3 years of Computer Science, IT, or related field experience
- OR
- Bachelor's degree and 3 to 5 years of Computer Science, IT, or related field experience
- OR
- Diploma and 7 to 9 years of Computer Science, IT, or related field experience
Preferred Qualifications:
- Hands-on experience with data practices, technologies, and platforms, such as Databricks, Python, Gitlab, LucidChart, etc.
- Proficiency in data analysis tools (e.g., SQL) and experience with data sourcing tools.
- Excellent problem-solving skills and the ability to work with large, complex datasets.
- Understanding of data governance frameworks, tools, and best practices.
- Knowledge of and experience with data standards (FAIR) and protection regulations and compliance requirements (e.g., GDPR, CCPA).
Good-to-Have Skills:
- Experience with ETL tools and various Python packages related to data processing, machine learning model development.
- Strong understanding of data modeling, data warehousing, and data integration concepts.
- Knowledge of Python/R, Databricks, cloud data platforms.
- Experience working in a Product team's environment.
- Experience working in an Agile environment.
Professional Certifications:
- AWS Certified Data Engineer (preferred).
- Databricks Certificate (preferred).
Soft Skills
- Initiative to explore alternate technology and approaches to solving problems.
- Skilled in breaking down problems, documenting problem statements, and estimating efforts.
- Excellent analytical and troubleshooting skills.
- Strong verbal and written communication skills.
- Ability to work effectively with global, virtual teams.
- High degree of initiative and self-motivation.
- Ability to manage multiple priorities successfully.
- Team-oriented, with a focus on achieving team goals.
