Design, develop, and maintain scalable and efficient data pipelines to support analytics, reporting, and business applications.
Integrate data from multiple sources (internal and external), ensuring data quality, consistency, and governance.
Model and optimize relational and non-relational databases, ensuring performance and availability.
Implement and monitor data ingestion, transformation, and provisioning processes in Big Data and/or cloud environments.
Ensure data security, integrity, and compliance with internal policies and applicable regulations.
Automate processes and data flows to reduce rework and increase operational efficiency.
Support Analytics, Data Science, and Product teams in proper access to and use of data.
Define and enforce data engineering best practices, including versioning, testing, and documentation.
Monitor and resolve incidents related to pipelines, infrastructure, and data quality.
Collaborate with technology and business teams to identify needs, propose solutions, and evolve the company’s data architecture.

Proficiency in Python
Proficiency in SQL (preferably PostgreSQL syntax)
Experience with Airflow (building DAGs)
Experience with AWS cloud services (preferably S3, Lambda, SQS, and RDS)
Experience building and/or maintaining a Data Lake with a medallion architecture
Experience with or knowledge of Pydantic
Experience with Apache Iceberg or Apache Hudi
Experience managing an Airflow cluster on Kubernetes

Data Engineer (Mid-level)

Key skills