Job description Client's is helping its client find a Senior Data Engineer to help design and implement cloud services for their data science platform, ensuring a streamlined experience for data scientists and engineers. In this role, you will provide architectural leadership, including data modeling, building real-time data pipelines, implementing data governance, optimizing performance, and enabling AI/ML workflows. We are looking for someone with 10+ years of data engineering experience, including data architecture design/management of large-scale data lakes and warehouses. The ideal candidate will also have expertise in Apache Iceberg, AWS Glue, AWS systems, and ERD design, as well as experience in Python, Scala or Java. As a Senior Data Engineer, you will: Provide architectural leadership, including data modeling, building real-time data pipelines, implementing data governance, optimizing performance, and enabling AI/ML workflows. Accelerate the development and fine-tuning of Agentic AI models to improve their capabilities and performance. Implement vertical benchmarking programs like EDGE to create strict evaluation criteria and enable comparison of various AI models and applications. Empower PXE Engineers to increase AI productivity and act as a force multiplier. Requirements 10+ years of data engineering experience including data architecture design/management of large-scale data lakes and warehouses Solid understanding and experience with Apache Iceberg and AWS Glue Deep experience with AWS systems (e.g., Lakeformation, IAM, and Redshift) Proficiency in Python, Scala, or Java for ETL and custom tooling Proven expertise in complex schema and ERD design, maintaining data dictionaries, and developing real-time/batch data pipelines using tools like Kafka, Spark, or similar. AI/ML integration familiarity, leadership abilities, and strong problem-solving skills Preferred Qualifications: Data governance: experience with implementing data governance frameworks, compliance, and security standards Leadership: demonstrated ability to lead teams, mentor junior engineers, and work in cross-functional environments Experience with vector databases/RAG, petabyte-scale data, advanced analytics tools, and industry experience in AI/ML, SaaS, or cloud technology Experience processing large amounts of data with Databricks or Snowflake Commitment: This is a full-time, 3-month contract position staffed via Client's. It will be onsite in Seattle, WA with the potential to be hybrid. Client's values diversity and is an Equal Opportunity Employer. We are interested in every qualified candidate who is eligible to work in the United States. Unfortunately, we are not able to sponsor visas or employ corp-to-corp. Client's Comments Headcount: 1 Client: Qualtrics Ops questions: Can you walk us through your experience designing large-scale data architectures or managing data lakes/warehouses? What tools and approaches did you use? Tell us about a project where you used Apache Iceberg and AWS Glue? (If Applicable, not a dealbreaker) What’s your experience building real-time or batch data pipelines? Which tools did you use (e.g., Kafka, Spark), and what were some key performance considerations? Describe how you've enabled AI/ML workflows from a data engineering perspective. How did you structure your data to support model development or deployment? Have you ever implemented a data governance framework? How did you ensure data security, compliance, and proper access controls across systems like AWS?