Director Data Engineering

About the role

The Director, Data Engineering role at Publicis Sapient is dedicated to leading 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 teams in designing and implementing data ingestion, validation, enrichment, batch, streaming, and event-driven pipelines.
• Design and implement cloud-native data platforms across leading public cloud platforms such as Amazon Web Services, Microsoft Azure, Google Cloud Platform, Snowflake, and Databricks.
• Implement Databricks or similar lakehouse platforms, including notebooks, jobs, Delta Lake, orchestration, optimization, and lakehouse implementation patterns.
• Develop 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.
• Build 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.
• Support AI/ML lifecycle needs such as model deployment support, monitoring, validation, shadow deployments, release management, evaluation infrastructure, and data quality measurement for predictive and generative systems.
• Apply AI engineering concepts in practical business environments rather than only academic or research settings.

Requirements

Exceptional data engineering skills with a distributed computing background and proven experience delivering large-scale, production-grade data platforms.

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 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

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 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.

Skills

Developer certifications for AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related cloud and data platforms.

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