This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Snowflake Data Engineer in Latin America.

As a Senior Snowflake Data Engineer, you will work on designing, building, and optimizing scalable data pipelines that enable high-quality analytics and reporting. You will contribute to the development and automation of complex data workflows, ensuring reliability, consistency, and performance across multiple systems. In this role, you will collaborate with cross-functional teams to provide technical expertise and improve the overall data framework. You will have the opportunity to leverage cloud platforms, modern data engineering tools, and programming languages to solve challenging problems and support data-driven decision-making. The environment is fast-paced, innovative, and supportive of professional growth and experimentation.

Accountabilities:

Design, implement, and productionize data pipelines in Snowflake to support business needs.
Assemble large, complex datasets that meet functional and non-functional requirements.
Create and maintain datasets to enable analytics and product functionality.
Identify, design, and implement internal process improvements, including automation, infrastructure redesign, and pipeline optimization.
Enhance data quality, reliability, and consistency through monitoring, failure detection, and retry processes.
Collaborate with cross-functional teams to improve the overall data architecture and technical frameworks.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Math, Statistics, or related field, or equivalent practical experience.
3–5 years of experience in data engineering using Snowflake.
2–4 years of experience with Python or another data-focused programming language (Scala, Go, R, Java, etc.).
Strong SQL skills and experience with data transformation tools like DBT, Airflow, Spark, or Pandas.
Cloud experience, preferably AWS, including services such as Redshift, Kinesis, Glue, Step Functions, SageMaker, and RDS.
Knowledge of building processes and infrastructure for dataset lifecycle management, including data structures, metadata, and workload management.
Experience working in Agile environments.
Excellent English communication skills.

Nice to have:

Experience with Kubeflow, EKS, Docker.
Knowledge of stream-processing systems such as Kafka, Storm, Spark-Streaming.
Statistical analysis and machine learning experience.
Ability to visualize and communicate complex data concepts effectively.

Benefits

Work on innovative projects using modern technologies.
Join a multicultural community of tech experts.
Opportunities for career growth and continuous learning.
Flexible and remote working environment.
Exposure to challenging, high-impact projects and data-driven initiatives.

Jobgether AI Matching Process

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, the 3 candidates with the highest match are automatically shortlisted.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias, focusing solely on your fit for the role. Once shortlisted, your profile is shared directly with the hiring company, which manages interviews or further assessments.

Thank you for your interest!

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