Sr. Data Engineer

The Sr. Data Engineer supports the design, development, and maintenance of data pipelines, ETL processes, and database systems to support AI and data science initiatives. This role involves ensuring data quality, scalability, and performance across all data engineering activities.

• Support the design, development, and maintenance of data pipelines, ETL processes, and database systems to support AI and data science initiatives.
• Collaborate with data scientists, AI/ML engineers, and other stakeholders to understand data requirements and ensure data availability and quality.
• Implement data governance, security, and regulatory standards in all data engineering activities.
• Optimize data pipelines and processes for scalability, performance, and cost-efficiency.
• Monitor and ensure the performance and reliability of data systems, identifying and resolving issues as needed.
• Stay updated with the latest advancements in data engineering technologies and best practices.
• Mentor and provide guidance to junior data engineers and other team members.
• Prepare and present data engineering reports and documentation to senior management and stakeholders.
• Participate in project planning and contribute to the development of project timelines and deliverables.
• Perform other duties relevant to the job as assigned by the Principal Data Engineer or senior management.

• Bachelor’s degree in Data Engineering, Computer Science, or a related field
• Relevant certifications (e.g., Google Cloud Professional Data Engineer, AWS Certified Big Data – Specialty) are preferred
• Minimum of 5 years of experience in data engineering or related fields
• Experience in designing and implementing data pipelines, ETL processes, and database systems for AI or technology-focused products
• Strong programming skills in languages such as Python, SQL
• Proficiency in data engineering tools and frameworks (e.g., Apache Spark, Kafka)
• Excellent problem-solving and analytical skills
• Strong communication and interpersonal skills
• Attention to detail and commitment to quality
• In-depth understanding of data engineering principles, ETL processes, and database management
• Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services
• Knowledge of data governance, security, and regulatory standards
• Ability to manage multiple tasks and prioritize effectively
• Strong attention to detail and commitment to delivering high-quality work
• Ability to work independently and as part of a team
• Programming languages (e.g., Python, SQL)
• Data engineering tools and frameworks (e.g., Apache Spark, Kafka)
• Data management systems (e.g., SQL, No
  
  SQL databases)
• Collaboration and communication tools (e.g., Slack, Microsoft Teams)