Mid-Level Data Engineer

• 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.

• 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.

• 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