Senior Analytics Engineer

The Senior Analytics Engineer is responsible for designing, building, and maintaining critical data pipelines, data models, and infrastructure solutions that enable data products across project delivery, operations, and business functions.

This is a hands‑on implementation role focused on delivering production‑ready data solutions. The ideal candidate has direct experience building data pipelines and analytics solutions used by business stakeholders and is comfortable working with complex, fragmented data across multiple systems.

• Deliver production‑ready ETL/ELT pipelines supporting project, financial, and operational data
• Translate business requirements into clean, scalable data models with minimal rework
• Improve performance, reliability, and usability of existing data pipelines and reporting solutions
• Migrate core data infrastructure from legacy systems to modern platforms, supporting the business’s shift to a data product‑first strategy
• Effectively collaborate with stakeholders in AEC, design, or project‑based environments

• Go All In. Own the Outcome. We go all in—with urgency, follow‑through, and pride in the result.
• Be Direct. Be Respectful. Be Authentic. We keep it real—with clarity, candor, and care.
• Grow with Purpose. We embrace growth—through learning, coaching, and continuous improvement.
• Think Bold. Execute Smart. We think boldly—and pair ambition with discipline.
• Make Space for Everyone. We value you—and build a culture where people can thrive and do their best work.

To perform this job successfully, an individual must be able to perform each essential duty and responsibility satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Critical features of this job are described under the headings below. They may be subject to change at any time due to reasonable accommodation or other reasons.

• Design, build, and maintain production data pipelines
• Develop reliable ingestion processes across structured and unstructured data sources
• Support integrations across enterprise and design‑related platforms
• Optimize pipelines for performance, scalability, security, and cost efficiency
• Ensure data is reliable, scalable, and accessible
• Partner with IT, Enterprise and Practices to enable smooth data operations

• Maintain cloud data stores that support analytics, reporting and other data products
• Own and optimize SQL queries, stored procedures, and scheduled jobs for data product workflows
• Build and maintain analytics‑ready data models and semantic layers
• Align data models to business logic, metrics, and data product needs
• Evaluate and recommend improvements to tools, data structures, and analytics approaches
• Ensure data quality, consistency, and usability through validation, governance, and documentation
• Deliver reliable datasets for business users and project teams

• Implement data validation, audits, and error handling
• Ensure compliance with data privacy, security standards, and IT policies
• Create documentation, metadata, data dictionaries, and best practices

• Translate business requirements into data models and pipeline solutions
• Work within established architecture patterns to deliver scalable and maintainable solutions
• Contribute to recommendations on data structures, tools, and reporting approaches

• Work with business stakeholders to understand business needs and define requirements
• Partner with stakeholders, analysts, and technical teams to deliver and support usable data products across functions
• Improve data‑driven decision making and reduce manual delivery processes
• Support documentation, knowledge sharing, and solution standardization

• Strong proficiency in SQL and data modeling with a track record of building scalable solutions
• Hands‑on…