- 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.
- 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.
- 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
Skills Required
- 7+ years of data engineering experience with ownership of end-to-end pipeline architecture
- Deep experience designing orchestration workflows in Apache Airflow
- Experience working with containerized data infrastructure in production (deploying services, diagnosing operational issues, contributing to reliability and scalability)
- Ability to evaluate and communicate architectural tradeoffs (freshness vs. cost, scalability vs. simplicity) to technical and non-technical stakeholders
- Experience building or significantly improving CI/CD practices for data pipelines, including automated testing, validation, and deployment
- Track record leading incident response and monitoring strategy, building alerting and observability proactively
- Experience influencing technical decisions across multiple teams or functions (engineering, product, data science)
- Experience mentoring other data engineers and reviewing architectural and modeling decisions
- Experience with Kubernetes-based data infrastructure
- Experience leading legacy ETL-to-modern-orchestration migrations end-to-end
- Familiarity with observability and monitoring tooling such as Datadog at platform-wide scale
- Experience building internal tooling or automation (including AI-assisted) that other engineers rely on
What We Do
For us, renting is personal — we’re a company built by renters for renters. We’re here to deliver renters a home they love at the value they deserve. Whether renters are embarking to a new city, in search of a quieter neighborhood, or making the jump to a two-bedroom apartment — we’re here to find them their perfect apartment match. We’ve made it our mission to revolutionize renting by making it a stress-free, simple, and holistic experience, connecting ready-to-move renters with compatible city living. Home is a feeling everyone should own, and we’re on a mission to help renters find it.
Why Work With Us
Want a job that makes a difference? There are over 100 million renters in the US, and at Apartment List, we strive every day to remove the hassle from renting and make the process better for every single one of them. Together, we can change the narrative around what it means to be a renter.
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