Job description

Role Overview Data Engineers at Fractal design, build, and maintain the robust data pipelines that power enterprise analytics platforms. Working with Snowflake, dbt, Airflow, and AWS infrastructure, you will ingest data from diverse sources, transform it to meet business requirements, and ensure data quality and performance at scale. This is a highly collaborative role requiring both deep technical expertise and strong communication skills, working closely with analytics engineers, BI teams, and business stakeholders to deliver reliable, production-grade data solutions. Key Responsibilities • Design and develop version-controlled data pipelines using dbt and Airflow on Snowflake and AWS • Ingest data from diverse sources (REST APIs, databases, S3, Splunk, SFTP) following internal standards • Implement data quality checks, monitoring, and alerting to ensure pipeline reliability • Optimize query performance and pipeline efficiency through systematic analysis and tuning • Maintain comprehensive documentation of data architecture, lineage, and dependencies • Participate in code reviews and mentor junior team members on best practices • Troubleshoot failed DAGs and resolve production data issues with urgency • Support feature enhancements and ad-hoc analysis requests from analytics teams • Implement geospatial data engineering solutions for location-based retail analytics • Ensure compliance with data governance and PII protection requirements • Collaborate with analytics engineers and BI teams on data modeling and schema design Required Skills LatAm Position (G7): 6-10 years of hands-on data engineering experience • 3+ years working with modern cloud data platforms (Snowflake, BigQuery, Redshift, Databricks) • 3+ years with orchestration tools (Airflow, Prefect, Dagster) • 2+ years building data transformation logic with dbt or equivalent • Expert-level SQL: complex queries, window functions, optimization • Proficiency in Python for data engineering tasks • Strong knowledge of Snowflake, dbt, and Airflow • AWS services: S3, Lambda, EC2, IAM, CloudWatch • Understanding of data quality testing and monitoring • Experience with version control (Git) and CI/CD practices Preferred Skills • Knowledge of Kubernetes, Docker, and containerization • Experience with geospatial data processing (GIS) • Understanding of relational databases and data modeling • Bachelors degree in Computer Science, Engineering, Mathematics, or related field • Masters degree preferred

Requirements

Must-haves

• Design and develop version-controlled data pipelines using dbt and Airflow on Snowflake and AWS
• Ingest data from diverse sources (REST APIs, databases, S3, Splunk, SFTP) following internal standards
• Implement data quality checks, monitoring, and alerting to ensure pipeline reliability
• Optimize query performance and pipeline efficiency through systematic analysis and tuning
• Maintain comprehensive documentation of data architecture, lineage, and dependencies
• Participate in code reviews and mentor junior team members on best practices
• Troubleshoot failed DAGs and resolve production data issues with urgency
• Support feature enhancements and ad-hoc analysis requests from analytics teams
• Implement geospatial data engineering solutions for location-based retail analytics
• Ensure compliance with data governance and PII protection requirements
• Collaborate with analytics engineers and BI teams on data modeling and schema design
LatAm Position (G7): 6-10 years of hands-on data engineering experience
• 3+ years working with modern cloud data platforms (Snowflake, BigQuery, Redshift, Databricks)
• 3+ years with orchestration tools (Airflow, Prefect, Dagster)
• 2+ years building data transformation logic with dbt or equivalent
• Expert-level SQL: complex queries, window functions, optimization
• Proficiency in Python for data engineering tasks
• Strong knowledge of Snowflake, dbt, and Airflow
• AWS services: S3, Lambda, EC2, IAM, CloudWatch

Nice-to-haves

• Knowledge of Kubernetes, Docker, and containerization
• Experience with geospatial data processing (GIS)
• Understanding of relational databases and data modeling
• Bachelors degree in Computer Science, Engineering, Mathematics, or related field
• Masters degree preferred

Application prep

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Keywords to mirror

awsrestsqlpythonci/cdkubernetesdocker

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Effort: medium