Velir is an established mid-sized agency with a top-tier portfolio of clients, ranging from the world’s largest non-profits to Fortune 500 brands. As of 2023, Velir acquired Brooklyn Data Company, a premier data and analytics consultancy focused on leadership, process improvement, implementation, and advanced analytics.

At Velir, we believe people are our greatest asset. Our culture is built on a foundation of trust, collaboration, and continued improvement. We strive for excellence in everything we do, embracing challenges as opportunities for growth. Our success is driven by a shared passion for making a positive impact on our customers, our communities and each other. We are a remote first company that offers competitive pay and excellent benefits.

Overview

At Brooklyn Data, Senior Analytics Engineers lead the design and implementation of scalable data models and pipelines that turn complex data into clear, trustworthy insights. They work closely with functional leadership and data solutions partners to shape data architectures and modeling strategies that align with client goals and deliver measurable impact. Because our clients are mostly US-based organizations, we look for the ability to communicate with professional proficiency in English, verbally and in writing.

Responsibilities

Engineering Leadership

• Lead complex technical implementations, shaping data architecture and transformation pipelines that prioritize performance, maintainability, and reusability.
• Drive adoption of best practices for dbt development, testing, and CI/CD while mentoring peers through code reviews, pairing, and technical guidance.
• Contribute to internal tooling, shared dbt packages, and scalable frameworks that raise the standard of analytics engineering across teams and clients.

Cross-Team Collaboration

• Partner with analysts, engineers, and stakeholders to translate complex and often ambiguous business requirements into well-structured data models and semantic layers.
• Serve as a technical and strategic advisor in cross-functional planning—balancing stakeholder requirements, data governance, and long-term scalability.
• Model strong communication and mentorship, helping other AEs and cross-functional teammates develop clarity and confidence in data architecture decisions.

Project Enablement

• Lead end-to-end delivery of analytics engineering workstreams, ensuring solutions are reliable, well-documented, and aligned with client objectives.
• Anticipate and address systemic data quality or modeling issues, implementing durable solutions that improve trust and efficiency across the stack.