This job is no longer available
This job expired on 24/08/2026. It no longer accepts applications.
Remote Data Engineer
Huzzle.com
Job description
About the role
We are looking for a skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. You will collaborate with analysts, scientists, engineers, and business stakeholders to ensure reliable, high‑quality data for analytics and machine learning initiatives.
Key responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines.
- Build and optimise data architectures, databases, and data warehouses.
- Integrate data from multiple sources, APIs, and third‑party platforms.
- Ensure data quality, consistency, reliability, and security.
- Monitor, troubleshoot, and document data workflows.
- Collaborate with cross‑functional teams to understand data requirements.
- Implement data governance best practices.
- Optimise storage, processing performance, and query efficiency.
- Support reporting, BI, and analytics initiatives.
Required profile
- 3+ years of experience in data engineering or data warehousing.
- Strong proficiency in SQL and database management.
- Experience with Python, Java, or Scala.
- Hands‑on experience with ETL/ELT tools and data pipeline development.
- Knowledge of cloud platforms (AWS, Azure, or GCP).
- Familiarity with modern data warehouses such as Snowflake, Redshift, BigQuery, or Databricks.
- Experience with orchestration tools like Airflow or Prefect.
- Understanding of data modelling, governance, and quality principles.
- Excellent analytical and communication skills.
Required skills
- SQL
- Python
- Java
- Scala
- ETL/ELT tools
- AWS
- Azure
- Google Cloud Platform
- Snowflake
- Redshift
- BigQuery
- Databricks
- Airflow
- Prefect
What we offer
- Competitive salary based on experience and location.
- Fully remote work with flexible arrangements.
- Career growth opportunities with innovative companies.
- Long‑term projects with global organisations.
- Continuous learning on modern data technologies.
- Collaborative culture alongside talented engineers and analysts.
- Access to cutting‑edge data platforms and tools.
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