БразилияIntermediate Data Ops Analyst
2 дн. назад
БразилияMiddle
data analysisincident managementdata qualitydata triage
Responsible for incident management, triage, data analysis, and ensuring data quality and issue resolution within a B2B commerce platform.
О компании
- AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.
- Join us to build the future of B2B commerce!
- BEES is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. At BEES, we dream big, lead with purpose, and develop technology that transforms the way retailers and sellers grow.
- Every line of code and every partnership is built in service of a single mission: to make commerce better for retailers and sellers around the world. Here, your work is not just important. It makes a difference!
Обязанности
- Incident Management & Triage
- Perform triage, initial diagnosis, and impact analysis of data incidents, classifying them byseverity, urgency, and business impact.
- Conduct multi-level data analysis: 1st level (data consistency and data quality checklists) and2nd level (data issue sourcing for onboarded data products).
- Identify the type and location of the issue (data production, processing, or consumption) byvalidating the full workflow/pipeline.
- Respond to bug reports in compliance with SLAs (deadlines, severity, escalation paths,notifications).
- Participate in War Rooms and crisis responses (Sev1/Sev2) as L2 support.
- Data Analysis & Troubleshooting
- Perform troubleshooting using SQL, PySpark/Scala (Databricks), and NoSQL (MongoDB).
- Execute complex ad-hoc data extractions for audits, one-off analyses, and incidentinvestigations.
- Validate data flows end-to-end — from source systems (CosmosDB) through processinglayers (Bronze/Silver/Gold) to consumption (dashboards, APIs, data sharing).
- Execute configuration updates: pipeline parameters, reference tables, and business rulesadjustments.
- Perform technical interventions in production SQL and NoSQL databases to correctapplication states when required.
- Root Cause Analysis & Continuous Improvement
- Develop action plans based on root cause analysis (RCA), connecting cross-teaminvestigative processes.
- Identify patterns in recurring issues and propose improvements/optimizations to engineeringand product teams. Contribute to proactive incident detection by monitoring instrumented alerts (job healthchecks, data consistency alerts).
- Identify, communicate, and manage operational risks before they escalate.
- Communication & Stakeholder Management
- Proactively communicate the status of each open ticket to impacted stakeholders.
- Facilitate communication between Operational and Analytical teams, bridging DataEngineering, Data Analytics, and Product/Application teams.
- Engage technical specialists and escalate to Incident Specialist when applicable.
- Follow up with project teams to ensure resolution deadlines and customer satisfaction.
- Connect with Data Product Managers (DPMs) to stay updated on KPIs, reports catalog, anddata entity catalog per domain.
- Knowledge Management & Documentation
- Create, maintain, and continuously improve a structured knowledge base (runbooks,troubleshooting guides, resolution records).
- Document evidence, resolution steps, and root causes for every incident.
- Contribute to data flow diagrams and operational documentation using collaborative tools(Lucidchart, Confluence).
- Maintain understanding of product data flows and platform architecture to support effectivediagnosis.
- Service Requests & Operational Execution
- Execute planned data operations and standard service requests (data extractions,configuration updates).
- Define and refine data-related SLAs in alignment with stakeholders (flows, severities, MTR).
- Provide follow-up on metrics related to ticket resolution, SLA compliance, and product impact.
Требования
- Bachelor's degree in Computer Science, Data Science, Information Technology, Engineering,or related field.
- Proven experience in Data Operations, Data Analysis, Support Engineering, or similar hybridrole.
- Strong SQL skills (Snowflake preferred) and ability to query and analyze large datasets.
- Experience with PySpark or Scala in Databricks environments.
- Familiarity with NoSQL databases (MongoDB, Cosmos DB).
- Understanding of data pipeline concepts and architectures (ETL/ELT, Data Lake layers —Bronze/Silver/Gold).
- Experience with data quality dimensions (accuracy, completeness, validity, consistency,timeliness, uniqueness).
- Familiarity with Git and git-flow practices.
- Intermediate knowledge of analytics infrastructure (Databricks/Unity Catalog, DataFactory,Power BI, Azure Storage/DataLake).
- Experience with incident management processes (ITIL-aligned) and SLA-drivenenvironments.
- Ability to read and interpret data flow diagrams, API contracts/Swagger, and systemarchitectures.
- Advanced English (written and verbal).
- Strong analytical and problem-solving mindset — you thrive on investigating root causes andconnecting dots across systems.
- Excellent communication skills — you can translate technical findings into clear updates forstakeholders at all levels.
- Proactive and responsive — you don't wait for problems to escalate; you anticipate and act.
- Organized and process-oriented — you value documentation, knowledge reuse, andoperational consistency.
- Collaborative — you bridge teams naturally (Engineering, Product, Analytics) and buildproductive working relationships.
- Comfortable working under pressure, managing multiple incidents, and prioritizing effectively.
- High level of self-motivation, curiosity, and desire to continuously learn and improveoperational maturity.
- Ability to work in a global, multi-country support context..
Будет плюсом
- Knowledge of DBT (SQL + Jinja).
- Basic understanding of DevOps Engineering (Azure Pipelines, Terraform).
- Experience with telemetry/observability tools (Segment, Mixpanel, OpsGenie).
- Familiarity with Azure Cost Analysis. Experience with data governance tools (Atlan, Unity Catalog).
- Knowledge of data sharing protocols (Azure Delta Sharing, Standard Data Offerings).
Условия
- Performance-based bonus*
- Attendance Bonus*
- Private pension plan
- Meal Allowance
- Casual office and dress code
- Days off*
- Health, dental, and life insurance
- Medicines discounts
- Gympass partnership
- Childcare subsidies
- Discounts on Ambev products*
- Clube Ben partnership
- Scholarship*
- School materials assurance
- Language and training platforms
- Transport allowance
- *Rules applied