AB InBev

Data Analyst

• Develop scalable analytical solutions using Power BI, Azure Databricks, SQL, and Python.
• Design and maintain dashboards, KPI frameworks, semantic models, and self-service analytics solutions.
• Transform data into actionable insights that support strategic business decisions.
• Apply statistical and analytical techniques to identify trends, opportunities, and business impacts.
• Perform exploratory analysis, ad hoc studies, and support experimentation initiatives.
• Partner with Product, Commercial and Technology teams to solve business problems through data.
• Ensure data quality, consistency, governance, and reliability across analytical products.
• Follow software engineering best practices, including Git-based development, version control, testing, and CI/CD processes.
• Communicate insights through effective data storytelling, visualizations, and executive presentations.

Senior Data Analyst

Data Engineer

• Define and Advance ML Platform Strategy
• Drive Adoption and Platform Delivery
• Enable Scalable Machine Learning Operations

Data Product Manager – Machine Learning Platform

• Support the development and maintenance of data pipelines, ingestion processes, and data transformations.
• Create and maintain SQL queries, Python scripts, and Spark-based workloads used for data processing and analytics.
• Assist in troubleshooting pipeline failures, data quality issues, and operational incidents.
• Work with senior engineers to implement schema mappings, transformation logic, and data validation rules.
• Ensure datasets meet expected schemas, data contracts, and quality standards.
• Support metadata management, dataset documentation, and lineage activities.
• Assist in maintaining data classification information according to company standards.
• Help automate repetitive operational and data management tasks to improve efficiency and reliability.
• Contribute to monitoring, alerting, and operational support for data pipelines and workflows.
• Participate in testing activities, including unit tests, transformation validation, and data quality checks.
• Follow established engineering standards, coding practices, and team development patterns.
• Learn and apply security, privacy, and compliance requirements when handling sensitive or regulated data.
• Collaborate with Data Governance, Security, and Compliance teams when required.
• Contribute to continuous improvement initiatives focused on data trust, reliability, and operational excellence.

Mid-Level Data Engineer

Support Engineer

• Collaborate with teams to design, build, deploy and monitor new and existing services;
• Build and evolve our change management and release processes;
• Identify and improve processes by automation;
• Focus on observability and logging with our services by proactive monitoring, alerting, and detecting anomaly in our systems;
• Take part in monitoring activities, being able to quickly mitigate issues in production while project teams are engaged;
• Mitigate issues in production by using a variety of methods like scripts, automations, infrastructure scaling;
• Raise 3rd party tickets when needed (Microsoft, CloudAMQP, NewRelic, etc);
• Update and improve runbooks;
• Deep-dive into root cause analysis with teams to prevent incidents;
• Collaborate with infrastructure and security teams to build scalable, secured and resilience systems;
• Practice Support Engineering methodologies such as defining SLO and error budget;
• Work closely with Operations team to ensure all processes are aligned with teams;
• Contribute to capacity planning and access management process;
• Be constantly challenged to create world-class solutions.