Intermediate Data Ops Analyst

abinbev; Campinas; On-site; BRL 120,000 - 230,000; Full time

Job summary

AB InBev through BEES is seeking a Data Operations Engineer to join the incident management and data analysis team. You will perform triage, diagnose data issues, and ensure data quality across the pipeline from source to dashboards.

The role requires strong SQL, PySpark/Scala in Databricks, NoSQL familiarity, and ITIL-aligned incident handling. You will collaborate with Data Eng, Analytics, and Product teams to meet SLAs.

Qualifications

• Bachelor's degree in CS/DS/IT or related field.
• Proven experience in Data Operations or similar hybrid role.
• Strong SQL skills and ability to query large datasets.
• Experience with PySpark/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 Git and git-flow practices.
• Intermediate knowledge of analytics infrastructure (Databricks, Power BI, Azure Storage/DataLake).
• Experience with incident management processes (ITIL-aligned) and SLA-driven environments.
• Advanced English (written and verbal).

Responsibilities

• Perform triage and impact analysis of data incidents.
• Conduct multi-level data analysis and issue sourcing.
• Identify issue type and location across the workflow/pipeline.
• Respond to bug reports per SLAs and escalation paths.
• Participate in War Rooms and crisis responses as L2 support.
• Communicate status to stakeholders and bridge teams.

Skills

SQL

PySpark/Scala

NoSQL databases

Data pipelines/ETL-ELT

Git workflows

English (Advanced)

Incident management ITIL

Stakeholder communication

Education

Bachelor's degree in Computer Science / Data Science / IT

Tools

Snowflake

MongoDB

Cosmos DB

Databricks

Power BI

Azure Data Lake

Job description

About us

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.

About BEES

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!

What you will do:

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 escalates.

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 escalated 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.

What you'll need

• 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).

Nice to have

• Knowledge of DBT (SQL + Jinja).
• Basic understanding of DevOps Engineeri