We are looking for a business-oriented Data and AI Engineering Manager to lead our data and AI engineering team in developing practical, high-value data services across our operating regions. In this role, you will oversee the creation of data-driven solutions for our products and business processes, focusing on areas such as user retention, marketplace optimization, and operational monitoring. You will also be responsible for improving our data pipelines and warehouse infrastructure to support daily business and product analytics.

What You Will Deliver:

• Lead, manage, and coach a team of data engineers and AI specialists.
• Collaborate with regional and headquarters teams to understand their business needs and apply data and AI-driven approaches to solve practical problems.
• Promote the use of effective and reliable data methodologies, continuously improving the company’s data capabilities.
• Communicate the value of the data team’s work to management and other teams, explaining technical concepts in a simple, understandable way.
• Provide guidance in deriving actionable insights from data to inform business decisions.
• Plan and prioritize data projects, developing strategies and roadmaps that align with business objectives and maximize efficiency.
• Guide the team in building and applying models and algorithms to create data-informed products and services, while monitoring their impact on business performance.
• Review data analysis to offer actionable insights for business and product optimization.
• Lead efforts to enhance data quality, availability, and integration across systems.
• Support and train colleagues in various business units with basic data exploration skills.

Who You Are:

• 5+ years of experience in data engineering or analytics.
• 2+ years of experience managing data or analytics teams.
• Proven experience in using data analytics and engineering to solve business problems and inform decisions.
• Hands-on experience with developing models, algorithms, and data solutions using common methods and technologies.
• Strong understanding of data technologies such as Python, SQL, GCP stack, Kafka, Kinesis, and ETL processes.
• Proficiency in basic statistical analysis, A/B testing, and time-series analysis.
• Preferably a background in logistics or a similar industry.
• Excellent communication skills, able to explain technical concepts in a way that is accessible to non-technical audiences.
• A commercial mindset with the ability to align data strategies with business goals.
• Passion for delivering value and creating positive impact for stakeholders.
• Eagerness to learn and improve continuously.
• A hands-on approach with a focus on getting things done and problem-solving.
• Patience and adaptability to work with stakeholders from different levels and backgrounds.