Data Engineer

Data Engineers are responsible for building and maintaining the architecture used for data storage and processing. They develop, construct, test, and maintain data management systems, ensuring that they meet organizational requirements. Data Engineers work closely with data scientists and analysts to provide them with usable data and are essential for data-driven decision making.

What are the responsibilities & duties of a Data Engineer

• Collaborate with Technology Teams, Global Analytical Teams, and Data Scientists across programs
• Improve database and reporting tools performance
• Create dashboards and visualization using Tableau
• Develop UI, tools, and applications to digitize processes
• Lead business growth and enhance product experiences by gaining experience in large-scale data processing systems (batch and streaming)
• Design and develop business intelligence solutions for complex business problems
• Work with stakeholders for business analytics tool development and data models
• Improve the quality of data by adding sources, coding rules, and producing metrics as requirements evolve
• Cooperate with other organizations on data governance, KPIs, and reporting tools
• Direct ETL development demonstrating understand key concepts of ETL/ELT
• Apply multi-dimensional and tabular design patterns
• Work within the Software Development Life Cycle (SDLC) across multiple environments

What are the required skills & experiences of a Data Engineer?

• Expertise in SQL and experience with database technologies (e.g., MySQL, PostgreSQL, Microsoft SQL Server)
• Proficiency in big data technologies (e.g., Hadoop, Spark)
• Experience with data pipeline and workflow management tools (e.g., Airflow, Luigi)
• Knowledge of scripting languages (e.g., Python, Java)
• Understanding of ETL (Extract, Transform, Load) processes
• Familiarity with cloud services (e.g., AWS, Azure, GCP)
• Strong problem-solving and analytical skills
• Ability to work in a team and communicate effectively
• Experience with version control tools (e.g., Git)
• Understanding of data warehousing concepts
• Knowledge of data modeling techniques
• Familiarity with machine learning algorithms and data science principles