Manager, Data Engineering

Manager, Data Engineering

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.

As a Manager, Data Engineering, you will play a central role in designing and building the data foundation that powers enterprise analytics, operational reporting, and future AI-driven capabilities. Working across Trading, Finance, Operations, and Technology teams, you'll help deliver scalable solutions that connect complex business systems, improve data accessibility, and drive better business outcomes. This is an opportunity to work on large-scale data engineering challenges, modern cloud technologies, and high-value business initiatives while helping shape the future of the organization's data platform.

Responsibilities

Data Engineering & Platform Development

• Design, develop, and maintain scalable data pipelines that support enterprise reporting, analytics, operational workloads, and emerging AI use cases.
• Build and optimize batch, streaming, and near real-time data integration solutions across diverse enterprise environments.
• Develop cloud-native data engineering solutions that meet growing business, operational, and analytical demands.
• Build resilient, high-performance platforms that prioritize scalability, reliability, maintainability, and operational excellence.
• Troubleshoot and optimize data processing performance, ensuring efficient and cost-effective platform operations.

Lakehouse & Modern Data Platforms

• Support the implementation and evolution of enterprise Lakehouse architecture strategies utilizing Databricks and modern cloud technologies.
• Design and develop ingestion, transformation, curation, and serving layers that enable trusted and scalable enterprise data consumption.
• Build reusable frameworks, patterns, and automation capabilities that accelerate onboarding of new data sources and business domains.
• Partner with architecture and platform engineering teams to establish standards, reference architectures, and engineering best practices.
• Contribute to the advancement of modern cloud data platforms that support analytics, data products, machine learning, and future AI initiatives.

Trading, Market & Enterprise Data Integration

• Integrate and reconcile data from trading platforms, market systems, ERP applications, operational technologies, and external data providers.
• Develop complex transformation logic and source-to-target mappings that support enterprise data movement and business processes.
• Support critical business capabilities involving trading operations, settlements, scheduling, risk management, market data, and financial reporting.
• Ensure high levels of data consistency, integrity, and traceability across interconnected platforms and business domains.
• Collaborate with business and technology teams to solve complex integration challenges across enterprise environments.

Data Modeling & Data Products

• Contribute to conceptual, logical, and physical data modeling initiatives that support enterprise reporting and analytics capabilities.
• Support the design and implementation of business-aligned data domains and reusable data products.
• Partner with product owners, analysts, architects, and business stakeholders to develop trusted, governed, and scalable data assets.
• Optimize data structures and storage strategies to support reporting, analytics, operational processing, and advanced analytical workloads.
• Enable self-service analytics and improved data accessibility through well-designed data products and engineering practices.

Data Governance & Data Quality

• Implement engineering processes supporting metadata management, lineage tracking, data quality monitoring, and governance initiatives.
• Develop validation, reconciliation, observability, and monitoring capabilities that improve transparency and trust in enterprise data.
• Collaborate with governance and stewardship teams to strengthen data quality standards and practices across the organization.
• Ensure engineering solutions align with enterprise governance, compliance, security, and risk requirements.
• Support continuous improvement efforts that enhance data reliability, usability, and adoption.

Architecture & Engineering Excellence

• Apply enterprise architecture standards, engineering best practices, and reusable design patterns to data engineering solutions.
• Participate in architecture reviews, design discussions, technology evaluations, and modernization initiatives.
• Contribute to technology roadmaps and implementation planning efforts supporting the organization's evolving data strategy.
• Identify opportunities to improve engineering efficiency through automation, modernization, and adoption of emerging technologies.
• Champion engineering best practices related to performance optimization, DevOps, CI/CD, testing, monitoring, and operational support.

Leadership & Collaboration

• Lead data engineering initiatives while partnering closely with architects, analysts, platform teams, product owners, and business stakeholders.
• Mentor and support engineers while fostering a culture of collaboration, innovation, accountability, and continuous learning.
• Facilitate requirements workshops, technical design reviews, and solution planning sessions.
• Develop and maintain architecture diagrams, technical specifications, workflow documentation, operational runbooks, and data mapping artifacts.
• Communicate complex technical concepts clearly and effectively to both technical and business audiences.
• Build strong relationships across functions and serve as a trusted partner in delivering enterprise data solutions.

Qualifications

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related technical field.
• 8+ years of experience in Data Engineering, Data Integration, Data Platform Development, or related technical roles.
• Proven experience designing and delivering enterprise-scale data engineering solutions and cloud-based data platforms.
• Strong experience building scalable data pipelines and data integration frameworks.
• Experience working with large-scale enterprise data environments and complex business systems.
• Strong understanding of data modeling, data architecture, and modern data engineering practices.
• Excellent communication, collaboration, and stakeholder management skills.

Preferred Qualifications

• Experience in Energy, Utilities, Oil & Gas, Commodity Trading, Financial Services, or other highly data-intensive industries.
• Hands-on experience with: Databricks, Azure Data Factory, Azure Synapse Analytics, Snowflake, Apache Spark, Delta Lake.
• Experience supporting Data Product, Data Mesh, or modern data platform initiatives.
• Experience with CI/CD pipelines, DevOps practices, Infrastructure as Code (IaC), and automated deployment frameworks.
• Experience integrating large-scale trading, market, operational, and financial systems.
• Experience working within Agile delivery organizations and cross-functional engineering teams.

Additional information

• Level: Manager
• Compensation Range: Depending on Experience - $140,000 - $180,000
• Benefits of Working Here
• 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.