Engineer - Data Engineering
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Data Pipeline Development: Design, develop, and maintain scalable data pipelines using PySpark to process large volumes of data from various sources.
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Data Integration: Integrate data from multiple data sources and formats, ensuring high data quality and reliability.
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Optimization: Optimize and tune data processing jobs for performance and cost-efficiency.
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Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality data solutions.
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ETL Processes: Develop and maintain ETL processes to extract, transform, and load data into data warehouses and data lakes.
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Data Quality: Implement data validation and monitoring processes to ensure data accuracy and consistency.
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Documentation: Document data engineering processes, workflows, and best practices.
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Troubleshooting: Identify, troubleshoot, and resolve data-related issues promptly.
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Experience: 3+ years of experience in data engineering or a related field.
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Education: Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
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Proficiency in PySpark and Python.
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Strong knowledge of big data technologies such as Hadoop, Hive, and Spark.
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Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services.
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Familiarity with data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake).
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Knowledge of relational and NoSQL databases (e.g., MySQL, MongoDB, Cassandra).
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Data Processing: Experience with ETL/ELT processes and data pipeline orchestration tools (e.g., Apache Airflow, Apache NiFi).
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Problem-Solving: Strong analytical and problem-solving skills.
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Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
PySpark, SQL, Python, Data Engineering
