Senior Associate, Data Engineering 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. 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.

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; Hands‑on experience with Azure Cloud Services, including Azure Data Lake Storage (ADLS), Azure Functions, Azure Kubernetes Service (AKS), and Azure Databricks.

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. In‑depth knowledge of Scala, Apache Spark, PySpark, Python, Java, and shell scripting. 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. Proficiency using Apache Airflow to orchestrate complex data workflows.

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. 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.

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. 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. An inclusive workplace that promotes diversity and collaboration.

Access to ongoing learning and development opportunities. Generous paid leave and holidays. 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 [email protected] Sapient is a digital transformation partner helping established organizations get to their future, digitally enabled state, both in the way they work and the way they serve their customers. We help unlock value through a start‑up mindset and up-to-date methods, fusing strategy, consulting, and customer experience with agile engineering and problem‑solving creativity.

United by our core values and our purpose of helping people thrive in the brave pursuit of next, our 20,000+ people in 53 offices around the world combine experience across technology, data sciences, consulting, and customer obsession to accelerate our clients’ businesses through designing the products and services their customers truly value. #

📌 Senior Associate Data Engineering (Azure/Databricks) Hybrid (Toronto)
🏢 Publicis Sapient
📍 Toronto