Manager Big Data Engineering - Databricks Lead - Hybrid

Company description

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. United by our core values and our purpose of helping people thrive in the brave pursuit of next, our 20,000+ people in 53 offices around the world combine experience across technology, data sciences, consulting, and customer obsession to accelerate our clients' businesses through designing the products and services their customers truly value.

Publicis 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.

• 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.

• 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, Big Query, Redshift, Vertica, or similar technologies.
• Experience with No
  
  SQL 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 work streams, and collaborating effectively across cross-functional and…