Mid-Level Data Engineer

Ambev

. Act as a Mid-Level Data Engineer at Zé Labs, with the autonomy to lead Data Engineering and Analytics Engineering initiatives within the squad.

Posted 9/15/2026full-timeRemote • BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Proficient in Data Engineering and Analytics Engineering, with strong capabilities in building and maintaining data pipelines, integrating diverse data sources, and ensuring data quality and observability. Experienced in using Python and SQL for data processing, along with knowledge of modern data platforms and best practices in data management.

Highest-signal resume keywords

Data Pipeline DevelopmentPython ProficiencySQL ProficiencySpark/PySpark KnowledgeData Quality Standards

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringAnalytics EngineeringData Pipeline DevelopmentSQLPythonSparkData IntegrationError HandlingObservabilityTesting

Soft Skills

Team LeadershipCollaborationProblem SolvingCommunication

Tools & Technologies

DatabricksDelta LakeUnity CatalogAWSAirflow

Industry Keywords

Data QualityMedallion ArchitectureCI/CDData ContractsSchema Evolution

Tech Stack

Tools & technologies

AirflowAWSCloudPySparkPythonSparkSQLUnity

About the role

Key responsibilities & impact

Act as a Mid-Level Data Engineer at Zé Labs, with the autonomy to lead Data Engineering and Analytics Engineering initiatives within the squad.
Increase the team’s autonomy over pipelines, ingestion, data quality, and observability.
Ensure that data for analytical products is available, reliable, documented, and sustainable.
Build and maintain analytical models with Analytics Engineers, including the Silver layer and semantic layer.
Develop, maintain, and enhance data pipelines.
Integrate data from relational databases, events, messaging systems, and files.
Structure and process data in the Bronze layer.
Increase Zé Labs’ autonomy over pipelines and processes dependent on the corporate Data team.
Define, review, and validate the infrastructure for jobs, workflows, and pipelines.
Review code and technical solutions, promoting engineering best practices.
Guide and support more junior professionals.
Implement and improve logs, metrics, alerts, and monitoring of pipeline health.
Investigate failures, inconsistencies, and performance issues.
Implement error handling, retries, idempotency, reprocessing, and backfills.
Document sources, pipelines, models, business rules, dependencies, and data contracts.
Contribute to SLAs, refresh criteria, and data quality standards.
Assess downstream impacts before making changes to schemas, pipelines, or models.
Participate in prioritizing and organizing the technical backlog with the Data Manager/Data PM.
Participate in technical discussions with Data Engineering, Platform Engineering, and stakeholders.

Requirements

What you’ll need

Bachelor’s degree completed.
Degrees in Technology, Computer Science, Engineering, Statistics, Mathematics, or related fields are preferred but not required.
Preferably three or more years of experience in Data Engineering, Analytics Engineering, or related fields.
Availability to work remotely.
Proficiency in Python for building pipelines, integrations, or automations.
Proficiency in SQL for querying, transforming, and modeling data.
Practical knowledge of Spark/PySpark or an equivalent distributed processing technology.
Experience building and maintaining data pipelines.
Experience integrating sources such as relational databases, files, APIs, events, or messaging systems.
Experience with modern data platforms or lakehouse architectures.
Knowledge of Git, pull requests, and code review practices.
Experience with error handling, retries, reprocessing, and backfills.
Practical knowledge of testing, observability, and pipeline monitoring.
Databricks, Delta Lake, and Unity Catalog are considered a plus.
AWS and cloud data services are considered a plus.
Databricks Workflows, Airflow, or equivalent orchestration tools are considered a plus.
Knowledge of medallion architecture, CI/CD for data pipelines, data contracts, schema evolution, impact analysis, job optimization, partitioning, compaction, processing costs, and security practices is considered a plus.

Benefits

Comp & perks

Medical Insurance
Dental Insurance
Telemedicine
Life Insurance
Gympass
Discount on company products
Christmas basket
Toys for employees’ children
Private pension plan
Meal or Food Voucher
Optional transportation allowance
Attendance bonus (14th salary)
Childcare or Babysitter Allowance
Profit Sharing
Meritocratic and inclusive environment
Development and career growth opportunities