Senior Big Data Engineer - Chicago Hybrid

Senior Associate Data Engineering - Chicago HybridAs a Senior Associate Data Engineer, you will be responsible for designing, developing, and maintaining scalable Big Data solutions. You will work with large datasets, real-time processing frameworks, and cloud-based data platforms to enable data-driven decision-making for our clients. Your impact will include combining 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.Your responsibilities will involve translating client requirements into system design and developing solutions that deliver measurable business value. You will lead, design, develop, and deliver large-scale data systems, data processing, data transformation, and data platform modernization initiatives. You will build and optimize batch and streaming data pipelines across modern cloud data platforms and distributed processing frameworks. Additionally, you will 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.You will 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 queryable form. You will conduct technical feasibility assessments and provide project estimates for the design and development of solutions. You will mentor, support, and grow junior team members while contributing hands-on to delivery.Your skills and experience should include 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 BigQuery, Redshift, Vertica, or similar platforms; NoSQL 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.AI Engineering & Modern Data Platform Experience: 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 the same lineage, provenance, and data contract rigor to context and retrieval sources that you would to a production warehouse. Experience with agentic harnesses or orchestration tools such as Pi, Hermes Agent, or similar platforms is a plus, but not required. Experience with Snowflake and zero-copy architecture patterns is a plus, particularly for retail, financial services, energy, or CPG-oriented use cases.Set Yourself Apart With: Developer certifications for AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related cloud/data platforms. Demonstrated experience applying AI engineering concepts in practical business environments rather than only academic or research settings. Hands-on experience supporting AI/ML and LLM lifecycle needs such as model deployment support, monitoring, validation, shadow deployments, release management, and evaluation or data quality measurement for both predictive models and generative systems. Experience in retail, financial services, energy, CPG, logistics, manufacturing, or other data-rich industries where applied AI and large-scale data engineering are used to solve operational or client-facing problems. Understanding of Agile, product, and delivery methodologies in consulting or client-facing environments.Salary Range: $136,000 - $160,000 annual salaryThe range above represents a grouping of ranges currently in use at Publicis Sapient. The actual range for this position may vary based on location and the specific skills required for the role.Benefits of Working Here: An inclusive workplace that promotes diversity and collaboration. Access to ongoing learning and development opportunities. Competitive compensation and benefits package. Flexibility to support work-life balance. Comprehensive health benefits for you and your family. Generous paid leave and holidays. Wellness program and employee assistance.As part of our dedication to an inclusive and diverse workforce, Publicis Sapient is committed to Equal Employment Opportunity without regard for race, color, national origin, ethnicity, gender, protected veteran status, disability, sexual orientation, gender identity, or religion. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at View email address on click.appcast.io.