Data Engineer building modern Data Lake architecture on AWS and implementing scalable ETL/ELT pipelines. Collaborating across teams for analytics and reporting on gaming platforms.
About the role
- Sr Staff AI Data Engineer responsible for implementing AI data pipelines at an insurance company. Collaborating with teams to enhance data capabilities and ensure scalability and reliability.
Responsibilities
- AI Data Engineering lead responsible for Implementing AI data pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions.
- Develop AI-driven systems to improve data capabilities, ensuring compliance with industry’s best practices.
- Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
- Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions.
- Stay up to date with industry advancements in AI and apply modern technologies and methodologies to our systems.
- Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.
- Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc.
- Ensure the reliability, availability, and scalability of data pipelines and systems through effective monitoring, alerting, and incident management.
- Implement best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies.
- Collaborate closely with DevOps and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems.
- Mentor junior team members and engage in communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns.
- Develop graph database solutions for complex data relationships supporting AI systems.
Requirements
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field
- 8+ years of strong hands-on data engineering experience including Data solutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark, Datamesh, Datalake or Data Fabric.
- Strong programming skills in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
- Experience in implementing data governance practices, including Data Quality, Lineage, Data Catalogue capture, holistically, strategically, and dynamically on a large-scale data platform.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Strong written and verbal communication skills and ability to explain technical concepts to various stakeholders.
- 3+ years of AI/ML experience, with 1+ years of data engineering experience focused on supporting Generative AI technologies.
Benefits
- Other rewards may include short-term or annual bonuses
- long-term incentives
- on-the-spot recognition
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