Data Engineer

Experience Level: 6-7 years Key Responsibilities Data and AI Engineering lead for large and complex data ecosystem leveraging data domains, data products, cloud and modern technology stack Real-Time Data Streaming: Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar. Data and AI Engineering lead responsible for Implementing Data and AI pipelines that bring together structured, semi-structured, and unstructured data from various sources, including but not limited to: databases, APIs, files, and more. This includes designing, building, and maintaining scalable and robust data processing workflows using technologies such as Apache Beam, AWS Glue, or similar. Additionally, this role will be responsible for developing and implementing AI/ML models that leverage large-scale datasets and complex algorithms to drive business insights and decision-making. Data and AI Engineering lead must have strong understanding of data engineering concepts, including but not limited to: data modeling, data governance, data quality, data security, and data compliance. Strong experience with cloud-based technologies such as AWS, Azure, or Google Cloud Platform is required. Experience with big data technologies such as Hadoop, Spark, Hive, or similar is also required. Strong understanding of AI/ML concepts including but not limited to: supervised and unsupervised learning, neural networks, deep learning, natural language processing, computer vision, and more is required. Data and AI Engineering lead must have excellent communication and collaboration skills, with ability to work effectively across multiple teams and stakeholders. Bachelor's or Master's degree in Computer Science, Data Science, or related field is required.