OverviewManager Data Engineering, Databricks LeadPublicis Sapient is seeking a Manager, Data Engineering with deep Databricks expertise to lead the design, delivery, and modernization of enterprise-scale data platforms. This role combines hands-on technical leadership, client engagement, architecture ownership, and team management. You will help clients build modern Lakehouse architectures, scalable data products, and AI-ready data foundations using Databricks and cloud-native technologies. The source role emphasizes Databricks, Python, cloud data platforms, AI engineering, and modern data architectures.
Your Impact- Combine your technical expertise, leadership skills, and problem-solving passion to work closely with clients, translating complex business challenges into modern data platform solutions that deliver measurable business value.
- Lead the architecture, design, and delivery of enterprise-scale data engineering solutions built on Databricks and cloud-native data platforms.
- Drive data modernization initiatives, helping clients migrate from traditional data architectures to modern lakehouse and cloud-based ecosystems.Lead the development and optimization of batch and streaming data pipelines using Databricks, Spark, and cloud-native data services.
- Design scalable data foundations that support analytics, machine learning, Generative AI, and AI-enabled experiences through high-quality data products and services.Partner with stakeholders to define data platform roadmaps, architecture standards, governance practices, and delivery approaches.
- Establish best practices for engineering excellence, performance optimization, data quality, observability, reliability, security, and operational support.
- Support AI-enabled engineering use cases by designing scalable retrieval patterns, context engineering approaches, and modern data services that power machine learning and agentic solutions.
- Conduct technical feasibility assessments, project estimation, architecture reviews, and solution planning activities for large-scale client engagements.
- Mentor and develop engineers while providing technical leadership, delivery oversight, and career guidance across multiple project teams.
- Contribute to practice growth through client engagement, solution development, capability building, hiring, and thought leadership.
QualificationsYour Skills and Experience - 10+ years of demonstrated experience leading the implementation of enterprise-scale data platforms and end-to-end data engineering solutions in production environments.
- Hands-on experience with Databricks as a primary data engineering platform, including Delta Lake, Databricks Workflows, Databricks SQL, notebooks, jobs, and modern Lakehouse architecture patterns.
- Strong experience designing and implementing scalable data platforms on one or more public cloud platforms including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
- Advanced proficiency in Python and practical experience using Python-based frameworks for data engineering, platform automation, and AI-enabled engineering workflows.
- Strong expertise in Apache Spark, PySpark, Spark SQL, and distributed data processing technologies.
- Experience implementing both batch and real-time data pipelines using technologies such as Spark Streaming, Glue ETL, Lambda, Dataflow, Azure Data Factory, Databricks, or similar frameworks.
- Experience with data modeling, dimensional modeling, data warehousing, and modern architectural patterns including Lakehouse and data mesh approaches.
- Experience with columnar data platforms such as Snowflake, BigQuery, Redshift, Vertica, or similar technologies.
- Experience with NoSQL technologies such as DynamoDB, Bigtable, Cosmos DB, or equivalent distributed databases.
- Experience implementing software engineering best practices including source control, CI/CD, automated testing, release management, infrastructure automation, and production support processes.
- Familiarity with MLOps concepts and supporting data engineering responsibilities related to model deployment, validation, monitoring, rollback, governance, and operational reliability.
- Experience leading engineering teams, managing delivery workstreams, and collaborating effectively across cross-functional and client-facing environments.
- Strong communication, stakeholder management, and consulting skills.
AI Engineering & Modern Data Platform Experience- Experience supporting AI-enabled solutions through the design and implementation of scalable, production-grade data platforms.
- Exposure to AI engineering concepts including context engineering, retrieval-augmented generation (RAG), agentic architectures, semantic search, and production data services supporting AI-powered experiences.
- Experience building and maintaining the data pipelines that support retrieval systems, including document ingestion, parsing, chunking, metadata extraction, embedding generation, and incremental indexing processes.
- Familiarity with vector databases, semantic search platforms, graph-based knowledge stores, and modern retrieval architectures.
- Exposure to cloud AI services such as Vertex AI, Azure AI Services, AWS AI Services, or similar AI platforms.
- Experience supporting AI and machine learning lifecycle requirements, including evaluation datasets, monitoring, operational telemetry, validation workflows, release management, and platform observability.
- Understanding of platform requirements for managing agent state, conversation history, session context, memory stores, and durable retrieval structures that support AI-enabled applications.
- Ability to apply enterprise data engineering principles such as lineage, governance, provenance, observability, and data contracts to AI-enabled platforms and retrieval systems.
- Experience supporting agentic frameworks, orchestration platforms, or emerging AI engineering technologies is a plus.
- Experience with Snowflake and zero-copy architecture patterns is a plus, particularly within retail, financial services, energy, logistics, manufacturing, or CPG industries.
Set Yourself Apart With- Databricks Data Engineer Associate, Professional, or Machine Learning certifications.
- Certifications in AWS, Microsoft Azure, Google Cloud, Snowflake, or related cloud and data technologies.
- Experience leading Databricks-based modernization initiatives and enterprise-scale Lakehouse implementations.
- Demonstrated experience applying AI engineering concepts and Generative AI technologies in production business environments.
- Hands-on experience supporting AI/ML and LLM lifecycle requirements, including deployment support, monitoring, validation, evaluation infrastructure, and operational governance.
- Experience in retail, financial services, energy, manufacturing, logistics, healthcare, CPG, or other data-intensive industries.
- Experience working in consulting, digital transformation, or client-facing delivery environments.
- Understanding of Agile, product, and modern delivery methodologies.
Additional informationSalary Range: $130,000-$180,000
The range shown represents a grouping of relevant ranges currently in use at Publicis Sapient. Actual range for this position may differ, depending on location and specific skillset required for the work.
- 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.
