Lead Data Engineer

We are looking for a Senior Data Engineer / Technical Lead with 5 years of experience and strong hands-on expertise in SQL Python Spark/PySpark and Azure (Azure Data Factory Synapse Apache Airflow).

The ideal candidate should have solid experience in ETL pipeline development data integration and data modeling as well as the ability to provide technical guidance to engineering teams and communicate effectively with international clients in English.

Location: Brazil or Argentina.

REQUIREMENTS:

- 5 years of experience in Data Engineering including hands-on development of ETL pipelines and data integration solutions

- Advanced proficiency in SQL and Python for data processing transformation and optimization

- Strong experience with Spark and PySpark for distributed data processing

- Hands-on experience with Azure data technologies and cloud-based data platforms

- Advanced proficiency with Azure Synapse Azure Data Factory and/or Apache Airflow for workflow orchestration and scheduling

- Strong understanding of Data Architecture Data Modeling Data Quality Data Integration and SQL and ETL performance optimization

- Proven ability to provide technical guidance to engineering teams and serve as a technical reference throughout project delivery

- Strong working proficiency in English for daily communication with international clients and teams

- Client-facing communication analytical and problem-solving skills

- Exposure to Microsoft Fabric Hadoop Hive Kafka Power BI or Tableau as a plus

- Experience with healthcare or pharmaceutical data environments as an advantageSelling PointsRESPONSIBILITIES

- Design and develop scalable ETL and data integration pipelines across Azure data environments

- Own technical implementation and solution quality ensuring reliable scalable and maintainable data processing

- Collaborate with clients Solution Architects and engineering teams to translate business requirements into effective data solutions

- Build and optimize data processing workflows using SQL Python Spark PySpark Azure Data Factory and Apache Airflow

- Develop data models and schemas aligned with business requirements architecture standards and downstream consumption needs

- Implement data quality and validation controls to maintain data integrity and consistency across platforms

- Optimize SQL queries ETL processes and data workflows to improve performance and reliability

- Guide the Data Engineering team on technical decisions engineering practices troubleshooting and successful solution deliveryTeam DescriptionIn this role you will design and deliver scalable data solutions within Azure environments while serving as a technical reference for the engineering team. Combining hands-on Data Engineering with technical leadership you will work closely with clients and architects to translate business needs into reliable solutions across the delivery lifecycle with a focus on data integration performance and quality.