Senior Associate Data Engineering – Hybrid in Toronto

Publicis 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 modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity.

Overview

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.

Your Impact:

• 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.
• 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.

Your Skills and Experience:

• 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.
• 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.
• 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.
• Exposure to agentic platforms or cloud AI services such as Vertex AI, Azure AI services, AWS AI services, or comparable platforms.
• 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.

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.
• Hands-on experience supporting AI/ML and LLM lifecycle needs.
• Experience in retail, financial services, energy, CPG, logistics, manufacturing, or other data-rich industries.
• Good understanding of capital markets domain requirements, particularly FINRA compliance.
• Understanding of Agile, product, and delivery methodologies in consulting or client-facing environments.

Additional information

• Salary Range: $115,000 - $160,000
• 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.