Senior Architect Data Engineering and Lakehouse Platforms

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.

Overview

We are seeking an experienced, hands-on Senior Architect/Data Engineering to drive the design, development, and governance of enterprise-scale data platforms built onDatabricks Lakehouse architecture. This role combines deep technical expertise with team leadership, solution architecture, stakeholder management, and data governance responsibilities. The ideal candidate will lead teams delivering scalable, secure, and governed data products while establishing engineering standards, best practices, and operating models across the organization.

Your Impact:

Technical Leadership

• Lead the design and implementation of enterprise data platforms using Databricks, Spark, and cloud-native technologies.
• Define data engineering standards, architecture patterns, and best practices.
• Review solution designs and provide technical guidance to engineering teams.
• Drive adoption of Lakehouse architecture, Delta Lake, and modern data engineering practices.
• Lead performance optimization, scalability, reliability, and cost-management initiatives.

Data Engineering Delivery

• Architect and develop batch, streaming, and real-time data pipelines.
• Build and support data products serving analytics, reporting, AI/ML, and business applications.
• Oversee data integration across ERP, trading, operational, and third-party systems.
• Establish reusable frameworks, accelerators, and reference architectures.
• Ensure robust monitoring, observability, and operational excellence.
• Implement governance controls using Unity Catalog, metadata management, and data lineage capabilities.
• Drive data cataloging, classification, access management, and auditability requirements.

Team Leadership

• Lead and mentor a team of Data Engineers.
• Conduct design reviews, code reviews, and technical coaching sessions.
• Work closely with Product Owners, Architects, Business Analysts, and Data Scientists to align delivery with business objectives.
• Support resource planning, estimation, and delivery governance.
• Foster a culture of continuous improvement, innovation, and engineering excellence.

Qualifications

Your Skills & Experience

• 10+ years of experience in Data Engineering and Data Platform implementation.
• 3+ years of experience leading Data Engineering teams or programs.
• Strong hands-on expertise with:
  • Databricks
  • Apache Spark /PySpark
  • Delta Lake
  • Unity Catalog
  • SQL
  • Python
• Experience with Azure, AWS, or GCP cloud platforms.
• Strong understanding of enterprise data governance, metadata management, data lineage, and data quality frameworks.
• Experience implementing CI/CD, Infrastructure as Code, and DevOps practices.
• Strong stakeholder management and communication skills.

Preferred Qualifications

• Experience with Azure Data Factory, Kafka, Event Hubs, Airflow, or similar integration technologies.
• Experience with Collibra, Purview, Alation, or other governance platforms.
• Familiarity with Data Mesh, Domain-Oriented Architecture, and Federated Governance models.
• Databricks Professional Data Engineer Certification.
• Experience delivering large-scale enterprise transformation or modernization programs.

Leadership Expectations

• Own the end-to-end delivery of strategic data initiatives.
• Influence architecture and technology decisions across multiple teams.
• Bridge business strategy with data platform capabilities.
• Establish governance frameworks that improve data trust, discoverability, and compliance.
• Drive adoption of engineering best practices, automation, and platform reuse.

Nice-to-Have Experience

• Trading and Risk Management systems (Endur/ETRM)
• Enterprise Data Platforms and Lakehouse implementations
• Data reconciliation and controls automation
• Master Data Management (MDM