БразилияIntermediate Data Ops Analyst
2 дн. назад
БразилияMiddle
data analysisincident managementtriagedata qualitydata pipeline
Intermediate Data Ops Analyst role responsible for incident management, triage, and multi-level data analysis for 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!
- is AB InBev’s B2B platform. Through our ecosystem, merchants and retailers across 29 countries can stock their businesses quickly, easily, and securely. At , 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