
Senior Data Engineer Position
2 weeks ago
Job Overview:
We are seeking an expert in data engineering to work on our team in a state-of-the-art, on-premise environment. The ideal candidate will have hands-on experience with ETL tools, such as Apache Nifi and Talend, along with a solid understanding of data processing technologies like Python scripting and relational databases. Additionally, you should be proficient in working with Spark, PySpark, and other relevant data technologies. While knowledge of cloud tools is beneficial, this position primarily focuses on on-premise data infrastructure.
This is an excellent opportunity to work on exciting projects that require developing scalable data pipelines, real-time data streaming, and optimizing data processing tasks using Python. As a key member of the team, you will be responsible for designing, developing, and optimizing ETL pipelines using open-source or cloud ETL tools. You will also write Python scripts to automate data extraction, transformation, and loading (ETL) processes.
Key Responsibilities:
- Design and Develop ETL Pipelines: Use ETL tools like Apache Nifi, Talend, and Airflow to design and develop efficient ETL pipelines.
- Python Scripting for Data Engineering: Write Python scripts to automate data extraction, transformation, and loading (ETL) processes. Ensure that the code is optimized for performance and scalability.
- Big Data Processing: Work with Apache Spark and PySpark to process large datasets in a distributed computing environment. Optimize Spark jobs for performance and resource efficiency.
- Job Orchestration: Use Apache Airflow or similar tools to schedule, monitor, and automate data pipeline workflows.
- Data Streaming: Design and implement real-time data streaming solutions using technologies like Apache Kafka or AWS Kinesis for high-throughput, low-latency data processing.
- File Formats & Table Formats: Work with open-source table formats like Apache Parquet, Apache Avro, or Delta Lake, and other structured/unstructured data formats for efficient data storage and access.
- Database Management: Work with relational databases like PostgreSQL, MySQL, and SQL Server for data storage, management, and optimization. Understand database concepts like indexing, normalization, and query optimization.
- SQL Expertise: Write and optimize complex SQL queries for data extraction, transformations, and aggregation across large datasets. Ensure queries are efficient and scalable.
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