Associate Director, Data Engineering

What's the role

We are looking for a data engineer that will set the direction for how data is built at Brainlabs, not just how it's delivered on a given project. You'll lead a team of data engineers, own the technical standards the practice works to, and be the person clients and internal stakeholders rely on when the architectural decisions get hard. You'll still get into the work where it matters, but your primary leverage is through the people and systems you build.

What's the company

Brainlabs is culture-first. You’ve probably heard other companies describe themselves as people, clients, or profit first – but we see people, clients and profit as the building blocks of our ecosystem. Culture powers all three and we know that when the ecosystem is balanced, we all win! That's why the culture we cultivate is a culture of winning together.

What you do

• Define and own the data engineering capability roadmap for NAMER, setting standards for how pipelines are designed, maintained, and scaled across the practice.
• Lead and develop a team of data engineers, providing technical direction, career development support, and hands-on guidance on complex problems.
• Architect scalable, production-grade data solutions on GCP, covering the full stack from ingestion and transformation through to warehousing and serving layers in BigQuery.
• Own the quality bar for data engineering output across the team, including pipeline reliability, query performance, data quality frameworks, and documentation standards.
• Partner with Data Science, Digital Analytics, and client teams to scope and deliver integrated data solutions, translating complex business requirements into sound technical architecture.
• Represent the data engineering practice in client conversations and new business contexts, communicating technical approaches clearly to non-technical stakeholders and contributing to proposals and SOW scoping.
• Drive adoption of engineering best practices across the team, including CI/CD, version control, infrastructure-as-code, data governance, and security on GCP.
• Evaluate and introduce new tooling and approaches, including orchestration platforms, streaming architectures, and GCP's AI/ML capabilities, where they materially improve what the team can deliver.

Who you are

• A technically deep data engineering leader who has grown past needing to prove themselves through individual output and is ready to be judged by what their team produces. You've made architectural decisions that mattered, you've managed engineers and made them better, and you know how to hold a technical conversation with a client without making it feel like a lecture.
• Deep hands-on expertise with GCP, including BigQuery, Cloud Functions, Cloud Run, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer.
• Strong Python and SQL skills, with a track record of building and reviewing production-grade pipelines, not just prototypes.
• Proven experience designing data warehouse and data mart architectures for analytics and reporting at scale.
• Solid grounding in both batch and streaming pipeline patterns, and the judgment to know which one a given problem actually needs.
• Experience with orchestration tools (Apache Airflow, Cloud Composer, or equivalent) and CI/CD practices including Git, Terraform, and pipeline deployment workflows.
• Working knowledge of data governance, security, and compliance considerations in cloud environments.
• Exposure to additional cloud stacks (AWS or Azure) and GCP AI/ML tooling is a plus.