Director Data Engineering

Director Data Engineering Publicis Sapient ("PS") is a leader in the digital transformation space, helping the best brands in the world get to their future, digitally enabled state, both in the way they work and the way they serve their customers. Fueled by a recognized heritage in large-scale IT and engineering, we combine market-leading capabilities in strategy, technology & engineering, platforming, experience design and more. We deliver ideas through execution across the ten business sectors in which we operate. As digital pioneers with 20,000 people and 53 offices around the globe, our experience spanning technology, data sciences, consulting and customer obsession is amplified by our parent company, Publicis Groupe, the world's largest multinational communications and marketing organization. Our culture is one of curiosity and relentlessness, and we embrace diversity and reward imagination. We seek achievers, leaders and visionaries, and our team looks to each person to bring skills and passion to help our clients solve their biggest business challenges. Role Overview Publicis Sapient is looking for a Director, Data Engineering to lead top-notch technologists and enable real business outcomes for enterprise clients. You will create impact for some of the world's biggest brands by translating complex business needs into scalable, AI-ready data solutions that deliver measurable value. Working with modern cloud data platforms, distributed processing frameworks, and AI/ML-enabled engineering patterns, you will help clients evolve toward a more digital, data-driven, and AI-enabled future. Successful candidates will bring deep data engineering expertise, hands-on technical credibility, experience leading teams, and a proven track record of creating, steering, and closing new business opportunities. Your Daily Duties & Impact: Act as a trusted advisor to clients by leveraging data, analytics, and AI-ready data foundations to drive customer engagement, operational insight, and large-scale digital transformation outcomes. Work closely with clients to evaluate and recommend design patterns and solutions for modern data platforms, with a focus on ETL, ELT, ALT, lambda, kappa, streaming, event-driven, lakehouse, and data mesh architectures. Define SLAs, SLIs, and SLOs with clients, product owners, and engineers to deliver reliable data-driven and AI-enabled experiences. Provide expertise, proof-of-concept, prototype, and reference implementations for cloud, on-prem, hybrid, and edge-based data platforms. Lead the design and delivery of large-scale data systems, data processing, data transformation, platform modernization, and production-grade data services. Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic, machine learning, and generative AI solutions. Guide the data engineering responsibilities required for AI/ML deployment support, validation, monitoring, rollback, evaluation, and operational reliability. Oversee telemetry and observability pipelines for AI-enabled services, including capture of prompt, response, trace, latency, token, cost, quality, and reliability data in queryable forms. Partner with leadership to bring opportunities to closure and transition them into delivery. Represent the PS portfolio through early-stage selling, proposal development, client oral presentations, and competitive win strategy. Provide technical inputs to agile processes, including epic, story, and task definition, and remove barriers throughout the lifecycle of client engagements. Create and maintain infrastructure-as-code for cloud, on-prem, and hybrid environments using tools such as Terraform, CloudFormation, Azure Resource Manager, Helm, and Google Cloud Deployment Manager. Mentor, support, and manage team members while continuing to model hands-on technical leadership and delivery excellence. Your Skills & Experience: Exceptional data engineering skills with a distributed computing background and proven experience delivering large-scale, production-grade data platforms. Ability to create new pursuits across target client accounts and bring forward clear, compelling, technically credible client propositions. Strong consulting, business, strategy, technical, and people leadership skills, with the ability to influence stakeholders, gain consensus, and build trusted client relationships. Hands-on experience with data processing and analytic engineering using SQL, DBT, Python, Spark, PySpark, Java, JavaScript, Scala, or similar tools. Strong Python proficiency and practical experience using Python-based tooling for data engineering, automation, platform development, and AI engineering workflows. Experience designing and implementing data ingestion, validation, enrichment, batch, streaming, and event-driven pipelines. Cloud-native data platform design experience across leading public cloud platforms such as Amazon Web Services, Microsoft Azure, Google Cloud Platform, Snowflake, and Databricks. Experience with Databricks or similar lakehouse platforms, including notebooks, jobs, Delta Lake, orchestration, optimization, and lakehouse implementation patterns. Data modeling, querying, and optimization experience across relational, NoSQL, timeseries, graph databases, data warehouses, data lakes, and modern lakehouse patterns. Hands-on expertise across the big data ecosystem for data integration, data storage, compute frameworks, analytics, advanced visualization, AI/ML platforms, and production data services. Familiarity with MLOps concepts and the data engineering responsibilities required to support AI/ML deployment, validation, monitoring, rollback, evaluation, and operational reliability. Experience building and maintaining pipelines behind retrieval systems, including document parsing, chunking, metadata extraction, embedding generation, incremental reindexing, and the vector, graph, semantic search, and knowledge retrieval structures they feed. 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 modeling and persisting agent state, including session context, conversation history, memory stores, lineage, provenance, and data contracts for context and retrieval sources. Experience building evaluation data infrastructure for AI systems, including ground-truth and golden datasets, offline evaluation pipelines, LLM-as-judge scaffolding, regression testing, and data quality measurement. Exposure to cloud AI services or agentic platforms such as Vertex AI, Azure AI services, AWS AI services, Pi, Hermes Agent, or comparable platforms is helpful; specific platform experience is less important than understanding how AI engineering differs from traditional data engineering. Experience with automated testing frameworks, data validation and quality frameworks, release management, production support, and data lineage frameworks. Metadata definition and management experience through data catalogs, service catalogs, and stewardship tools such as OpenMetadata, DataHub, Alation, AWS Glue Catalog, Google Data Catalog, or similar. Ability to lead teams that rapidly learn a client's current digital ecosystem and produce a future-state data landscape vision and strategy aligned to transformation agenda and business goals. Point of view on build vs. buy decisions, performance considerations, hosting options, commercial models, business intelligence, reporting, analytics, and AI-enabled product and platform capabilities. Experience interacting with clients, vendors, and Publicis Groupe peers with a focus on strategic optimization, quality control, delivery excellence, and adherence to the Digital Business Transformation vision. Experience interviewing and assessing prospective team members, new hires, vendors, and other contributors across a project community. Proposal creation experience, including staffing plans, delivery timelines, solution narratives, technical assumptions, and inputs to budget discovery. Ability to present to teams, clients, and the wider engineering community both within and outside of Publicis Groupe. Set Yourself Apart With Developer certifications for AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related cloud and data platforms. Demonstrated experience applying AI engineering concepts in practical business environments rather than only academic or research settings. Prodigious Worldwide