Design, develop, and maintain scalable ETL/ELT pipelines and data workflows.
Optimize data storage, processing, and retrieval using cloud-based technologies, with a strong focus on AWS.
Work with structured and unstructured data across various databases and data lakes.
Ensure data integrity, security, and compliance with industry standards.
Collaborate with cross-functional teams to define data requirements and support analytics and machine learning initiatives.
Monitor and improve data infrastructure performance, ensuring reliability and scalability.
Implement best practices for data governance, quality, and observability.
Develop and maintain robust data injection processes to ensure seamless data flow across systems.
Design and optimize data models to support analytical and operational use cases.

5+ years of experience in data engineering or a related field.
Strong expertise in AWS data services (e.g., Redshift, Glue, Athena, S3, Lambda, Kinesis, etc.).
Experience with big data technologies such as Apache Spark, Apache Airflow, or similar tools.
Strong knowledge of SQL and data modeling, schema design.
Strong hands-on experience in Python
Knowledge of CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation) is a plus.
Strong problem-solving skills and ability to work in a fast-paced environment.
Excellent communication skills and ability to collaborate with technical and non-technical stakeholders.
Fluent English (B2) with both written and spoken skills used for clear and active communication with native speakers. **Nice to Have:
Experience with real-time data streaming and event-driven architectures.
Exposure to machine learning workflows and MLOps practices.
Knowledge of security and compliance standards in data engineering.
Experience with GCP or Azure is a plus (BigQuery, Databricks, Azure Data Factory, etc.)

Data Engineer

Key skills