Senior Associate Big Data Cloud Engineering - Atlanta Hybrid

Senior Associate Data Engineer

Publicis Sapient is looking for a Senior Associate Data Engineer to be part of our team of top-notch technologists. You will lead and deliver technical solutions for large-scale digital transformation projects. Working with the latest data and AI engineering technologies in the industry, you will be instrumental in helping our clients evolve for a more digital and AI-enabled future.

• Combine your technical expertise and problem-solving passion to work closely with clients, turning complex ideas into end-to-end data solutions that transform our clients' business.
• Translate client requirements into system design and develop solutions that deliver measurable business value.
• Lead, design, develop and deliver large-scale data systems, data processing, data transformation, and data platform modernization initiatives.
• Build and optimize batch and streaming data pipelines across modern cloud data platforms and distributed processing frameworks.
• Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions.
• Automate data platform operations and manage post-production systems, observability, quality, reliability, and operational processes, including telemetry pipelines that capture prompt, response, trace, latency, token, and cost data for AI-enabled services in a query able form.
• Conduct technical feasibility assessments and provide project estimates for the design and development of solutions.
• Mentor, support, and grow junior team members while contributing hands-on to delivery.

• Demonstrable experience implementing end-to-end data pipelines and production-grade data platforms.
• Hands-on experience with at least one leading public cloud data platform:
  Amazon Web Services, Microsoft Azure, or Google Cloud Platform;
• Experience with Databricks as a data engineering platform is strongly preferred, including working with notebooks, jobs, Delta Lake, or similar lakehouse patterns.
• Strong Python proficiency and practical experience using Python-based tooling for data engineering, automation, platform development, or AI engineering workflows.
• Implementation experience with column-oriented database technologies such as Big Query, Redshift, Vertica, or similar platforms;
  No
  
  SQL database technologies such as DynamoDB, Bigtable, Cosmos DB, or similar; and traditional database systems such as SQL Server, Oracle, or MySQL.
• Experience implementing data pipelines for both streaming and batch integrations using tools and frameworks such as Glue ETL, Lambda, Google Cloud Dataflow, Azure Data Factory, Spark, Spark Streaming, or similar technologies.
• Experience with data modeling, warehouse design, fact/dimension implementations, and modern lakehouse or data mesh patterns.
• Experience with code repositories, continuous integration, automated testing, release management, and production support practices.
• Familiarity with MLOps concepts and the data engineering responsibilities required to support AI/ML deployment, validation, monitoring, rollback, and operational reliability.
• Ability to handle module or track-level responsibilities while contributing to tasks hands-on.
• Good communication skills and willingness to work as part of a collaborative, cross-functional team.

• Exposure to AI engineering patterns, including context engineering, retrieval-augmented generation support patterns, agent architectures, and production data services that support AI-enabled experiences.
• Experience building and maintaining the pipelines behind retrieval systems, including document parsing, chunking, metadata extraction, embedding generation, and incremental reindexing, alongside the vector databases, graph databases, semantic search, and knowledge retrieval structures they feed.
• Exposure to agentic platforms or cloud AI services such as Vertex AI, Azure AI services, AWS AI services, or comparable platforms; specific platform experience is less important than understanding how AI engineering differs from traditional data engineering.
• Practical experience deploying agents, integrating agent frameworks, or supporting agentic workflows in production or near-production environments is a plus.
• Experience building evaluation data infrastructure for AI systems, including ground-truth and golden datasets, offline evaluation pipelines, and the data scaffolding behind LLM-as-judge and regression testing.
• Experience modeling and persisting agent state, including session context, conversation history, and memory stores, treating them as a durable storage and data modeling problem rather than an application detail.
• Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions, applying…