Senior Data Engineer

Responsibilities

• Serves as the technical anchor and mentor for the data engineering team, setting standards, reviewing work, and guiding design and architecture decisions.
• Owes the relationship with other departments and data analysts, gathers their data needs, and ensures the data warehouse supports them.
• Acts as the subject-matter expert on our data, advising analysts on how data is structured, how sources relate, and how best to query it.
• Designs, develops, and maintains the data warehouse in Google Cloud Platform (GCP), ensuring scalability, performance, and reliability.
• Buils and manages robust ELT pipelines to extract, transform, and load data from various sources into the data warehouse.
• Defines and maintains data models that make data consistent and easy to understand for analysts and business users.
• Manages and maintains databases, including MS SQL Server and PostgreSQL, ensuring data integrity and security.
• Develops and maintains small software applications to support data management tasks and improve operational efficiency.
• Collaborates with stakeholders to gather data requirements and translate them into technical solutions.
• Troubleshoots data-related issues and provides timely solutions to ensure data availability and accuracy.
• Stays up-to-date with the latest data engineering technologies and best practices, and shares them with the team.

Requirements

• Bachelor's or Master's degree in Computer Science or a related field.
• Minimum of four years of experience as a Data Engineer with a strong understanding of data warehousing concepts and best practices.
• Ability to guide and mentor other engineers and influence technical direction.
• Prominent experience partnering with analysts and stakeholders to understand their needs and translate them into data solutions.
• Strong data modeling skills and the ability to explain how data relates and how it should be used.
• Hands-on experience with GCP cloud services, including BigQuery, Cloud Storage, and Dataflow.
• Proficiency in SQL and experience with database management systems such as MS SQL Server and PostgreSQL.
• Experience building and maintaining ETL pipelines using tools like Apache Airflow or Google Cloud Composer.
• Proficiency with Python and pandas.
• Experience with other programming languages and paradigms such as C#, nodeJS and Ruby.
• Knowledge of managing Linux and Windows servers.
• Experience with data modeling and data visualization tools.
• Experience with software development methodologies such as Agile.
• Knowledge of version control systems like Git.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.