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About the role
You will be responsible for leading the development and deployment of advanced data engineering and automation solutions at ERM. This mid-level role requires technical leadership, cloud architecture expertise, and collaboration across functions to support ERM's global initiatives. Key Responsibilities: - Design and implement scalable data pipelines and ETL processes using Apache Airflow, PySpark, and Databricks. - Lead the development of reusable, version-controlled codebases using Git. - Apply advanced NLP, OCR, and image processing techniques to extract insights from unstructured data. - Architect and deploy solutions on Azure, AWS, and GCP using services like Azure Data Factory, S3, Lambda, and BigQuery. - Build large-scale data processing solutions using Hadoop, Spark, or Databricks. - Monitor and maintain statistical models and data engineering workflows. Qualifications Required: - Bachelor's or Master's degree in Data Science, Computer Science, Engineering, or a related field. - 5-8 years of experience in data science, automation, or data engineering. - Advanced proficiency in Python, Git, and cloud platforms (Azure, AWS, GCP). - Strong experience with NLP, OCR, machine learning, and image processing. - Expertise in data pipeline architecture and cloud deployment. - Excellent problem-solving, communication, and leadership skills. You will be responsible for leading the development and deployment of advanced data engineering and automation solutions at ERM. This mid-level role requires technical leadership, cloud architecture expertise, and collaboration across functions to support ERM's global initiatives. Key Responsibilities: - Design and implement scalable data pipelines and ETL processes using Apache Airflow, PySpark, and Databricks. - Lead the development of reusable, version-controlled codebases using Git. - Apply advanced NLP, OCR, and image processing techniques to extract insights from unstructured data. - Architect and deploy solutions on Azure, AWS, and GCP using services like Azure Data Factory, S3, Lambda, and BigQuery. - Build large-scale data processing solutions using Hadoop, Spark, or Databricks. - Monitor and maintain statistical models and data engineering workflows. Qualifications Required: - Bachelor's or Master's degree in Data Science, Computer Science, Engineering, or a related field. - 5-8 years of experience in data science, automation, or data engineering. - Advanced proficiency in Python, Git, and cloud platforms (Azure, AWS, GCP). - Strong experience with NLP, OCR, machine learning, and image processing. - Expertise in data pipeline architecture and cloud deployment. - Excellent problem-solving, communication, and leadership skills.