● Analyze and organize raw data.
● Build data systems and pipelines.
● Interpret trends and patterns.
● Conduct complex data analysis and report on results.
● Prepare data for prescriptive and predictive modeling.
● Build algorithms and prototypes.
● Combine raw information from different sources.
● Explore ways to enhance data quality and reliability.
● Identify opportunities for data acquisition.
● Develop analytical tools and programs.
● Collaborate with data scientists and architects on several projects.
● Assemble large, complex sets of data that meet non-functional and functional business requirements.
● Identify, design, and implement internal process improvements including redesigning infrastructure for greater scalability, optimizing data delivery, and automating manual processes.
● Build required infrastructure for optimal extraction, transformation, and loading of data from various data sources.
● Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition.
● Work with stakeholders including the executive, product, data, and design teams to support their data infrastructure needs while assisting with data-related technical issues.
Requirements
● Previous experience as a data engineer or in a similar role.
● Technical expertise with data models, data mining, and segmentation techniques.
● Excellent analytical skills.
● Ability to identify trends, patterns, and insights from data.
● Knowledge of programming languages (e.g. Python).
● Hands-on experience with SQL database design.
● Understanding of reporting and data visualization tools such as Power BI and Tableau.
● Digital marketing analytics tools including Google Analytics and Google Tag Manager.
● Understanding of ETL framework and tools.
● Great numerical and analytical skills.
● Strong attention to detail.
● Bachelor's degree in Computer Science, IT, or similar fields; a Master’s degree is a plus.