Specialist Data Engineer

About the Company

iFood is a leading Brazilian technology company and a reference in Latin America. We connect thousands of restaurants to millions of consumers daily through innovative solutions, including food delivery, marketplace, pharmacy, and pet services, as well as our fintech, iFood Pago.

Responsibilities

• Define data governance policies and processes with a focus on Data Quality, reliability, lineage, and legislation
• Design and build solutions to facilitate adherence to governance policies, defining quality dimensions and metrics
• Participate in the definition and evolution of the Governance framework to promote a data culture
• Support new technology initiatives to ensure governance is defined from the start, helping domains adopt ownership and interoperability
• Develop governance monitors and indicators, such as reliability scorecards, automated tests, and continuous monitoring alerts
• Develop and disseminate the use of reusable libraries, data profiling, and anomaly detection at scale
• Collaborate with architects and engineers to ensure data pipelines comply with defined policies

Requirements

• Previous experience with Data Governance, Data Stewardship, or Metadata Management
• Deep knowledge of data quality dimensions and practical application
• Experience with monitoring, alerting, and troubleshooting data problems (Data Incident Response)
• Experience in Data Engineering or Software Engineering (pipelines, microservices, git, testing, orchestration)
• Solid understanding of data lifecycles and modern architecture (Data Lake, Lakehouse, Data Warehouse)
• Knowledge of Cloud platforms (AWS, GCP, or Azure)
• Proficiency in SQL and Python for data analysis, transformation, and validation
• Experience with metadata management, data lineage, and data classification

Preferred Qualifications

• Certifications in data governance frameworks (e.g., DAMA-DMBOK, DCAM)
• Practical experience implementing Data Contracts, Data Quality, and Observability (Great Expectations, Soda, dbt, Monte Carlo, etc.)
• Experience with Machine Learning Ops (data drift, concept drift, feature validation)
• Knowledge of Data Warehouse, Databases, and Kafka
• Experience with Airflow, Spark, Databricks, or dbt
• Knowledge of applied statistics for anomaly detection and profiling