What is this position about?
- Design, build, and maintain scalable data pipelines and ingestion frameworks supporting batch processing, streaming, and change data capture (CDC) patterns.
- Develop and optimize data processing workflows using Apache Spark and PySpark, ensuring reliable and performant transformation across large datasets.
- Implement and manage data lake architectures on AWS, leveraging S3 as the primary storage layer and Apache Iceberg for table format management, schema evolution, and time-travel capabilities.
- Work with AWS-native services including Glue for ETL orchestration, Athena for serverless querying, and Lambda for event-driven data processing.
- Manage data access controls and security configurations using AWS IAM, ensuring compliant and least-privilege access across the data platform.
- Collaborate with data analysts, data scientists, and platform engineers to understand data requirements and deliver production-ready solutions.
- Monitor pipeline health, troubleshoot data quality issues, and implement improvements to ensure data reliability and freshness.
- Contribute to the documentation of data flows, architecture decisions, and engineering standards within the team.
- Advanced hands-on experience with AWS services, including:
- AWS Glue
- Amazon S3
- Amazon Athena
- AWS IAM
- Apache Spark / PySpark for data processing.
- Data lake architectures, ideally with knowledge of Apache Iceberg.
- Data ingestion patterns, including batch processing, streaming, and CDC patterns
- Experience with SQL for data querying, validation, and exploratory analysis
- Solid understanding of data modeling and large-scale data processing
- Ability to work collaboratively in cross-functional teams and communicate technical concepts clearly to non-technical stakeholders.
- Experience with infrastructure-as-code or pipeline orchestration tools (e.g., Airflow, Step Functions, dbt) is a plus.
- Familiarity with AI-assisted development workflows and engineering productivity tools
What about languages?
English: Advanced (required for effective communication with global teams)
How much experience must I have?
2+ years of experience in Data Engineering or related roles, with hands-on work building and maintaining cloud-based data pipelines and lake architectures on AWS.
Our Perks and Benefits:
📚 Learning Opportunities:
- Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.
- Access to AI learning paths to stay up to date with the latest technologies.
- Study plans, courses, and additional certifications tailored to your role.
- Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
- English lessons to support your professional communication.
👨🏽💻 Travel opportunities to attend industry conferences and meet clients.
👩🏫 Mentoring and Development:
- Career development plans and mentorship programs to help shape your path.
🎁 Celebrations & Support:
- Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
- Company-provided equipment.
⚖️ Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.