Job Description

We are seeking a Data Engineer to support the development of data products that power critical business functions. This role focuses on building reliable, scalable data pipelines and improving data availability for a variety of stakeholders. You will work in a collaborative, cross-functional Agile environment and partner closely with technical and business teams to deliver high-quality data solutions. Enterprise experience strongly preferred.

Key Responsibilities

- Design, build, and maintain scalable data pipelines

- Develop and optimize ETL processes to support data products

- Work with structured and unstructured data across SQL and NoSQL systems

- Collaborate with cross-functional stakeholders to understand data requirements

- Ensure data quality, reliability, and availability across systems

- Manage and orchestrate workflows using pipeline management tools

- Contribute to Agile sprint planning and delivery commitments

- Troubleshoot and resolve data pipeline and workflow issues

Qualifications

Must-Have Skills

- Strong Python programming experience

- 5+ years of experience in Data Engineering or Data Warehousing

- 5+ years building data pipelines using ETL tools

- 3+ years hands-on experience with big data technologies such as Snowflake, Redshift, Hive, Kafka, Spark, or similar

- Extensive experience with SQL and NoSQL databases

- Experience with workflow management tools such as Airflow

- Strong communication and stakeholder management skills

- Experience working in Agile, cross-functional teams

- Ability to manage competing priorities and deliver within sprint commitments

Nice-to-Have Skills

- Experience with dbt

- Experience supporting data needs for finance, accounting, payments, or tax functions

- Advanced degree in Math, Statistics, Computer Science, or related field

Additional Information

Required Tools & Platforms

- Python

- SQL and NoSQL databases

- ETL tools (dbt preferred)

- Airflow or similar workflow orchestration tools

- Big data platforms such as Snowflake, Redshift, Hive, Kafka, or Spark

Location, Time & Engagement

- Location: LATAM (remote)

- Engagement: Contract

- Allocation: Full-time (40 hours per week)

- Duration: Through March 31, 2027