.Edelman is a voice synonymous with trust, reimagining a future where the currency of communication is action.
Our culture thrives on three promises: boldness is possibility, empathy is progress, and curiosity is momentum.At Edelman, we understand diversity, equity, inclusion and belonging (DEIB) transform our colleagues, our company, our clients, and our communities.
We are in relentless pursuit of an equitable and inspiring workplace that is respectful of all, reflects and represents the world in which we live, and fosters trust, collaboration and belonging.We are currently seeking a Senior Data Engineer with 5-7 years of experience.The ideal candidate will have the ability to work independently within an AGILE working environment and experience working with cloud infrastructure leveraging tools such as Apache Airflow, Databricks, DBT, and Snowflake.
Familiarity with real-time data processing and AI implementation, including generative AI, is highly advantageous.Why You'll Love Working With Us:At Edelman, we believe in fostering a collaborative and open environment where every team member's voice is valued.
Our data engineering team thrives on innovation and embraces cutting-edge technologies to solve real-world challenges.We are at an exciting point in our journey, leveraging Generative AI (GenAI), Large Language Models (LLMs), and advanced Retrieval-Augmented Generation (RAG) techniques to build intelligent, data-driven systems that deliver powerful PR insights.
You'll also work on developing agentic workflows that autonomously orchestrate tasks, enabling scalable and dynamic solutions.This is an excellent opportunity to make a significant impact on projects that push the boundaries of AI-powered insights and automation.If you're passionate about building high-performance data systems, working with cutting-edge AI frameworks, and solving complex challenges in a supportive, forward-thinking environment, you'll thrive here!Responsibilities:Design, build, and maintain scalable and robust data pipelines to support analytics and machine learning models, ensuring high data quality and reliability for both batch & real-time use cases.Design, maintain, and optimize data models and data structures in tools such as Snowflake and Databricks.Leverage Databricks and Cloud-native solutions for big data processing, ensuring efficient management of Spark jobs and seamless integration with other data services.Utilize PySpark and/or Ray to build and scale distributed computing tasks, enhancing the performance of machine learning model training and inference processes.Monitor, troubleshoot, and resolve issues within data pipelines and infrastructure, implementing best practices for data engineering and continuous improvement.Integrate generative AI capabilities into data pipelines and workflows to support advanced use cases such as data augmentation, automated content generation, and natural language processing