ANALYTICS ENGINEER (LONDON)

Fospha is the marketing measurement platform for eCommerce brands. We have found product/market fit in the last two years and quickly become a market leader for measurement with numerous awards and rocket-ship growth to match. We are the only business of our type to be a certified partner of Meta, TikTok and Snap, and have worked with our customers -some of the best-known eCommerce brands in the world to drive massive growth and value. We are now expanding globally and are looking for excellent candidates to join the next phase of our journey.

PROFESSIONAL REQUIREMENTS:

• Excellent knowledge of PostgreSQL and SQL technologies.
• Fluent in Python.
• Understanding of data architecture, pipelines and ELT flows/technology/methodologies. Understanding of agile methodologies and practices.
• Experience using dbt (Data Build Tool).
• Experience using pipeline technologies within AWS.
• Knowledge of data modeling and statistics.

THE ROLE

As a Analytics Engineer, you will be a key contributor to our data-driven success by designing, developing, and maintaining scalable data pipelines and architectures. You will play a crucial role in ensuring data integrity, optimizing data storage, and collaborating with cross-functional teams to support business initiatives. Additionally, you will drive continuous improvement in data processes, promote a culture of data excellence, and provide insights to support strategic decision-making.

KEY RESPONSIBILITIES:

• Implement and maintain ELT (Extract, Load, Transform) processes using scalable data pipelines and data architecture.
• Collaborate with cross-functional teams to understand data requirements and deliver effective solutions.
• Ensure data integrity and quality across various data sources.
• Support data-driven decision-making by providing clean, reliable, and timely data.
• Define the standards for high-quality data for Data Science and Analytics use-cases and help shape the data roadmap for the domain.
• Design, develop, and maintain the data models used by ML Engineers, Data Analysts and Data Scientists to access data.
• Conduct exploratory data analysis to uncover data patterns and trends.
• Identify opportunities for process improvement and drive continuous improvement in data operations.
• Stay updated on industry trends, technologies, and best practices in data engineering.