About the Role

Here's what you'll do as part of the team

• Own and evolve data pipeline architecture across core domains — ingestion, transformation, modeling, and serving — making project-level architectural decisions independently and evaluating tradeoffs between freshness, cost, scalability, and simplicity.
• Lead the design and implementation of platform-level improvements: warehouse cost management, compute efficiency, and access control architecture, treating reliability, observability, and cost efficiency as core design constraints rather than afterthoughts.
• Identify and lead technical initiatives that improve the platform's long-term health — proactively surfacing investments (orchestration, CI/CD, data access, developer experience) before they become blockers, and making the case for them.
• Drive large, technically complex projects or multiple concurrent medium-sized initiatives that span teams, taking responsibility for outcomes rather than just execution.
• Lead monitoring and testing strategy for your domain: proactively close observability gaps across the org, build alerting ahead of failures, and serve as the go-to engineer for the hardest production issues.
• Influence technical decisions and architectural direction beyond the Data & Analytics team, partnering directly with EPD stakeholders on infrastructure decisions that affect their roadmaps.
• Actively mentor other data engineers and analytics engineers, reviewing architectural and modeling decisions, and operate as a technical peer to senior engineers across teams.
• Integrate AI meaningfully into data engineering workflows — building tooling and automation that creates leverage for the whole team, not just individual output, and coaching others on effective, validated use.

Here are the skills and experience you'll need to be successful

• 7+ years of data engineering experience, including a demonstrated track record of owning end-to-end pipeline architecture, not just implementing to spec.
• Deep experience designing orchestration workflows in Apache Airflow, including making architectural tradeoffs across ingestion, transformation, modeling, and serving layers.
• Experience working with containerized data infrastructure in production, including deploying services, diagnosing operational issues, and contributing to platform reliability and scalability.
• Demonstrated ability to evaluate and communicate architectural tradeoffs (freshness vs. cost, scalability vs. simplicity) to both technical and non-technical stakeholders.
• Experience building or significantly improving CI/CD practices for data pipelines, including automated testing, validation, and deployment.
• A track record of leading incident response and monitoring strategy for a domain, including building alerting and observability ahead of failures rather than reacting to them.
• Experience influencing technical decisions across multiple teams or functions, including partnering with engineering, product, or data science stakeholders outside your immediate team.
• Experience mentoring other data engineers, including reviewing architectural and modeling decisions.

• Experience with Kubernetes-based data infrastructure
• Experience leading a legacy ETL-to-modern-orchestration migration end-to-end, not just contributing to one.
• Familiarity with observability and monitoring tooling such as Datadog at a platform-wide scale.
• Experience building internal tooling or automation (including AI-assisted) that other engineers rely on.

Here's the Pay Range:

• Zone 1: $171,000 - $207,000 TTC (including $154,000 - $182,000 base salary) + equity
• Zone 2: $158,000 - $191,000 TTC (including $142,000 - $168,000 base salary) + equity
• Zone 3: $145,000 - $176,000 TTC (including $130,000 - $155,000 base salary) + equity