Seeking a Senior Staff Engineer to drive our data engineering initiatives. This role involves architecting and implementing robust data solutions that support critical business functions. You will leverage cutting-edge big data technologies and cloud platforms to manage and process vast datasets effectively.

This position is ideal for an experienced professional passionate about building scalable, high-performance data systems. You'll have the opportunity to influence data strategy and collaborate with diverse teams to deliver impactful data products.

Design, develop, and maintain scalable data pipelines using PySpark to process large volumes of data from diverse sources. Integrate data from multiple formats, ensuring high quality and reliability. Optimize data processing jobs for performance and cost-efficiency. Collaborate with data scientists and analysts to understand data requirements and deliver solutions. Develop and maintain ETL/ELT processes for data warehouses and lakes. Implement data validation and monitoring to ensure data accuracy. Document data engineering processes and best practices. Troubleshoot and resolve data-related issues promptly.

Possess at least 3 years of dedicated experience in data engineering or a closely related domain. Hold a Bachelor’s degree in Computer Science, Information Technology, Engineering, or a comparable field. Demonstrate proficiency in PySpark and Python, alongside a strong understanding of big data technologies like Hadoop, Hive, and Spark. Experience with cloud platforms (AWS, Azure, GCP) and their data services is essential. Familiarity with data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake) and databases (relational and NoSQL) is required. Proven experience with ETL/ELT processes and data pipeline orchestration tools such as Apache Airflow or Apache NiFi. Excellent analytical, problem-solving, and communication skills are critical for explaining complex technical concepts.