onsite
Data Engineer - Applied Information Sciences
Data Engineer
Design, build, and maintain scalable data pipelines using Python, SQL, and cloud services. Implement ETL workflows, ensure data quality, and support analytics teams with reliable, high‑performance data solutions.
About the role
Key Responsibilities
- Develop and maintain robust ETL pipelines to ingest, transform, and load data from diverse sources into cloud data warehouses.
- Design data models and schemas that support analytical and reporting needs while ensuring scalability and performance.
- Implement workflow orchestration using Apache Airflow (or similar) to schedule, monitor, and troubleshoot data jobs.
- Collaborate with data scientists, analysts, and product teams to understand requirements and deliver clean, reliable data sets.
- Optimize data processing on AWS services (e.g., S3, Redshift, Glue) to reduce latency and cost.
- Establish data quality checks, logging, and alerting mechanisms to maintain pipeline integrity.
Requirements
- 3+ years of experience building data pipelines with Python and SQL.
- Proficiency in ETL concepts and tools, including Apache Airflow or comparable orchestrators.
- Hands‑on experience with AWS data services such as S3, Redshift, or Glue.
- Strong understanding of data modeling, warehousing, and performance tuning.
- Ability to work collaboratively in an agile environment and communicate technical concepts to non‑technical stakeholders.