
ETL Data Engineer
1 day ago
Key Responsibilities
- Design, develop, and maintain robust data pipelines and ETL/ELT workflows using PySpark, Python, and SQL.
- Build and manage data ingestion and transformation processes from various sources including Hive, Kafka, and cloud-native services.
- Orchestrate workflows using Apache Airflow and ensure timely and reliable data delivery.
- Work with large-scale big data systems to process structured and unstructured datasets.
- Implement data quality checks, monitoring, and alerting mechanisms.
- Collaborate with cross-functional teams including data scientists, analysts, and product managers to understand data requirements.
- Optimize data processing for performance, scalability, and cost-efficiency.
- Ensure compliance with data governance, security, and privacy Skills & Qualifications :
- 5+ years of experience in data engineering or related roles.
- Strong programming skills in Python and PySpark.
- Proficiency in SQL and experience with Hive.
- Hands-on experience with Apache Airflow for workflow orchestration.
- Experience with Kafka for real-time data streaming.
- Solid understanding of big data ecosystems and distributed computing.
- Experience with GCP (BigQuery, Dataflow, Dataproc)
- Ability to work with both structured (e.g., relational databases) and unstructured (e.g., logs, images, documents) data.
- Familiarity with CI/CD tools and version control systems (e.g., Git).
- Knowledge of containerization (Docker) and orchestration (Kubernetes).
- Exposure to data cataloging and governance tools (e.g., AWS Lake Formation, Google Data Catalog).
- Understanding of data modeling and architecture principles.
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