Lead Data Engineer

Position Summary

The Lead Data Engineer is responsible for defining, implementing, and maintaining the organization’s data architecture, platforms, data management practices, and reporting capabilities. This role serves as the technical owner of the organization’s data environment and establishes the standards, processes, and technologies used to collect, integrate, store, transform, govern, and consume data across the business.

This position will evaluate business and technical requirements, recommend appropriate data architectures and platforms, and lead the selection and implementation of technologies supporting data integration, analytics, reporting, and business intelligence. The Lead Data Engineer is expected to bring broad experience designing, implementing, and managing enterprise data solutions, along with sound architectural judgment, rather than expertise limited to a specific vendor or technology stack.

The role is highly hands-on. In addition to defining architecture and practices, the Lead Data Engineer will build and maintain data pipelines, models, integrations, reports, dashboards, and supporting infrastructure. The position will work closely with software developers, business stakeholders, and system owners to establish reliable interfaces between operational systems and the data platform while maintaining clear technical ownership of the organization’s data architecture and platforms.

As the organization’s data capabilities grow, this position will provide technical leadership for the data function, establish technical and operational practices, mentor additional team members, and help build a scalable data and analytics capability. The Lead Data Engineer will also serve as the organization’s primary technical authority for reporting, analytics, and business intelligence, working with stakeholders across the business to define requirements, develop solutions, and ensure consistent, accurate, and reliable use of organizational data.

Key Responsibilities

Data Strategy and Architecture

• Define and maintain the organization’s overall data architecture, including data integration, storage, transformation, modeling, analytics, reporting, and archival.
• Evaluate and recommend data architectures, platforms, and supporting technologies based on business requirements, scalability, maintainability, cost, and security.
• Lead the evaluation, selection, implementation, and ongoing evolution of the organization’s data platforms and supporting technologies.
• Establish architectural standards and practices that ensure organizational data is structured, integrated, managed, and consumed consistently and reliably.

Data Platform Management

• Design, build, and maintain reliable data pipelines, transformations, and data models supporting operational and analytical needs.
• Establish and maintain appropriate practices for orchestration, monitoring, error handling, recovery,

testing, deployment, and change control across data processes.

• Manage the performance, reliability, scalability, capacity, and cost of the data platform and supporting technologies.
• Troubleshoot complex data, transformation, performance, and platform issues across the data environment.
• Maintain appropriate technical documentation for data architecture, pipelines, models, interfaces, dependencies, and operational processes.

Data Integration and Application Collaboration

• Work closely with software developers and system owners to design reliable interfaces between operational applications and the data platform.
• Define appropriate integration methods, patterns, and data structures based on business, application, reporting, and analytical requirements.
• Lead the integration of internal applications, SaaS platforms, customer systems, and other data sources into the organization’s data environment.

Data Quality, Integrity, and Governance

• Establish and maintain standards and processes for data quality, integrity, consistency, traceability, and reliability across the organization’s data environment.
• Implement appropriate monitoring, validation, reconciliation, and testing practices to identify and resolve data quality issues and ensure the accuracy of transformed and reported information.
• Work with business and system owners to establish authoritative data sources, ownership, definitions, lineage, and appropriate governance of shared organizational data.
• Ensure data platforms and processes meet organizational requirements for security, access, privacy, retention, recovery, and regulatory compliance.

Reporting, Analytics, and Business Intelligence

• Own the technical strategy, architecture, platforms, and practices used for enterprise reporting, analytics, dashboards, and business intelligence.
• Work directly with business stakeholders to define reporting and analytical requirements and develop reliable, maintainable reports, dashboards, data models, and other business intelligence solutions.
• Establish common data models, metrics, definitions, and reporting standards that promote consistency and accuracy across the organization.
• Ensure reporting and analytical outputs are accurate, traceable, appropriately governed, and maintained throughout their lifecycle.

Data Practices and Technical Leadership

• Establish standards, practices, documentation, and operational processes for the organization’s data and analytics function.
• Provide technical direction and oversight for team members contributing to data platforms, integrations, reporting, analytics, and business intelligence.
• Review data designs, models, pipelines, reports, and technical approaches to ensure consistency with established architecture, standards, and practices.
• Mentor team members, promote knowledge transfer, and help develop the roles and capabilities needed to support the organization’s growing data and analytics needs.

Required Qualifications

• Significant professional experience in data architecture, data management, business intelligence, analytics, or related disciplines, including responsibility for production data environments.
• Demonstrated experience designing, implementing, and maintaining modern data architectures, platforms, pipelines, transformations, and data models.
• Strong understanding of data architecture and modeling concepts, including data warehouses, data lakes, lakehouses, relational and analytical data models, and ETL/ELT approaches.
• Advanced SQL skills and hands-on experience integrating data across applications, databases, APIs, SaaS platforms, and other data sources.
• Experience with modern cloud-based data platforms and associated storage, processing, integration, and orchestration technologies.
• Demonstrated experience implementing enterprise reporting and business intelligence solutions, including reports, dashboards, analytical datasets, and reusable data models.
• Strong understanding of data quality and governance practices, including validation, reconciliation, lineage, ownership, security, and access control.
• Demonstrated ability to evaluate technologies and architectural approaches, provide technical leadership, collaborate across technical and business functions, and independently lead complex data initiatives from design through ongoing operation.

Preferred Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related discipline, or equivalent professional experience.
• Experience designing and implementing enterprise data environments using major cloud, data, analytics, and business intelligence platforms.
• Experience integrating enterprise SaaS platforms, including Salesforce or comparable systems, as significant operational data sources.
• Experience with formal data governance, lineage, cataloging, master data management, or regulated data environments.
• Experience establishing or growing an organizational data, analytics, or business intelligence function.