Principal Data Engineer
Summary
Designs, builds, and maintains scalable data pipelines and ETL processes to power AI and data science initiatives for a media company.
The Principal Data Engineer handles the design, development, and maintenance of data pipelines, ETL processes, and database management to support AI and data science initiatives. This role involves ensuring data quality, scalability, and performance across all data engineering activities.
- Design, develop, and maintain datapipelines, ETL processes, and database systems to support AI and data scienceinitiatives.
- Collaborate with data scientists, AI/MLengineers, and other stakeholders to understand data requirements and ensuredata availability and quality.
- Implement data governance, security, andregulatory standards in all data engineering activities.
- Optimize data pipelines and processes forscalability, performance, and cost-efficiency.
- Monitor and ensure the performance andreliability of data systems, identifying and resolving issues as needed.
- Stay updated with the latest advancementsin data engineering technologies and best practices.
- Mentor and provide guidance to junior dataengineers and other team members.
- Prepare and present data engineeringreports and documentation to senior management and stakeholders.
- Participate in project planning andcontribute to the development of project timelines and deliverables.
- Perform other duties relevant to the job asassigned by the Head of Data & AI Engineering or senior management.
- Bachelor's degree in Data Engineering,Computer Science, or a related field
- Relevant certifications (e.g., Google CloudProfessional Data Engineer, AWS Certified Big Data - Specialty) are preferred
- Minimum of 8 years of experience in dataengineering or related fields
- Experience in designing and implementingdata pipelines, ETL processes, and database systems for AI ortechnology-focused products
- Strong programming skills in languages suchas Python, SQL
- Proficiency in data engineering tools andframeworks (e.g., Apache Spark, Kafka)
- Excellent problem-solving and analyticalskills
- Strong communication and interpersonalskills
- Attention to detail and commitment toquality
- In-depth understanding of data engineeringprinciples, ETL processes, and database management
- Familiarity with cloud platforms (e.g.,AWS, Azure, Google Cloud) and their data services
- Knowledge of data governance, security, andregulatory standards
- Ability to manage multiple tasks andprioritize effectively
- Strong attention to detail and commitmentto delivering high-quality work
- Ability to work independently and as partof a team
- Programming languages (e.g., Python)
- Data engineering tools and frameworks(e.g., Apache Spark, Kafka)
- Data management systems (e.g., SQL, NoSQLdatabases)
- Collaboration and communication tools(e.g., Slack, Microsoft Teams)