Engineer - Data Engineering

Engineer - Data Engineering

• Key Responsibilities:

• Data Pipeline Development: Design, develop, and maintain scalable data pipelines using PySpark to process large volumes of data from various sources.

• Data Integration: Integrate data from multiple data sources and formats, ensuring high data quality and reliability.

• Optimization: Optimize and tune data processing jobs for performance and cost-efficiency.

• Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality data solutions.

• ETL Processes: Develop and maintain ETL processes to extract, transform, and load data into data warehouses and data lakes.

• Data Quality: Implement data validation and monitoring processes to ensure data accuracy and consistency.

• Documentation: Document data engineering processes, workflows, and best practices.

• Troubleshooting: Identify, troubleshoot, and resolve data-related issues promptly.

• Required Qualifications:

• Experience: 3+ years of experience in data engineering or a related field.

• Education: Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.

• Technical Skills:

• Proficiency in PySpark and Python.

• Strong knowledge of big data technologies such as Hadoop, Hive, and Spark.

• Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services.

• Familiarity with data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake).

• Knowledge of relational and NoSQL databases (e.g., MySQL, MongoDB, Cassandra).

• Data Processing: Experience with ETL/ELT processes and data pipeline orchestration tools (e.g., Apache Airflow, Apache NiFi).

• Problem-Solving: Strong analytical and problem-solving skills.
• Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders