Director Data Engineering

About the role

The Director, Data Engineering role at Publicis Sapient is dedicated to leading a team of technologists who will enable real business outcomes for enterprise clients. This role involves translating complex business needs into scalable, AI-ready data solutions that deliver measurable value.

Responsibilities

• Create new pursuits across target client accounts and bring forward clear, compelling, technically credible client propositions.
• Lead the design, implementation, and maintenance of large-scale, production-grade data platforms.
• Design and implement data ingestion, validation, enrichment, batch, streaming, and event-driven pipelines.
• Collaborate with clients to understand their current digital ecosystems and develop a future-state data landscape vision and strategy aligned to their transformation agenda and business goals.
• Support the AI/ML lifecycle needs such as model deployment support, monitoring, validation, shadow deployments, release management, evaluation infrastructure, and data quality measurement.
• Build and maintain 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.
• Explain AI engineering patterns, including context engineering, retrieval-augmented generation support patterns, agent architectures, and production data services that support AI-enabled experiences.
• Develop and maintain 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.
• 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.
• Make informed decisions about build vs. buy, performance considerations, hosting options, commercial models, business intelligence, reporting, analytics, and AI-enabled product and platform capabilities.
• Present to teams, clients, and the wider engineering community both within and outside of Publicis Groupe.

Requirements

• 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, 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.

Qualifications

• 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, 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.

Skills

• 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, 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.

Benefits

• Flexible vacation policy; time is not limited, allocated, or accrued.
• 16 paid holidays throughout the year.
• Generous parental leave and new parent transition program.
• Tuition reimbursement.
• Corporate gift matching program.

Pay

$168,000 to $252,000

Schedule

N/A