Senior Data Engineer

Apartment List

14d•$113,000 - $158,400

About The Position

We are seeking a Senior Data Engineer (L3) to help build and operate the reliable, scalable data systems that power analytics, experimentation, and decision-making across Apartment List. In this role, you will be a strong end-to-end executor responsible for delivering production-grade data pipelines and workflows that meet defined service-level agreements (SLAs) for freshness, quality, and cost. You will work closely with Analytics Engineering, Data Science, Product, and Engineering partners to deliver durable data platform improvements that support company-wide initiatives. The ideal candidate is comfortable owning medium-sized data platform projects end-to-end—from design through deployment and operational support—while working within established platform architecture and engineering patterns. This role emphasizes execution excellence, reliability, and operational ownership within existing platform standards. Platform-level architecture and system design are owned at more senior levels, but this role plays a critical part in ensuring that the platform functions reliably at scale.

Requirements

5+ years of experience in data engineering, with a track record of delivering reliable production data pipelines and systems.
Strong experience designing and maintaining orchestration workflows using Apache Airflow.
Experience building scalable data pipelines using modern cloud data platforms such as BigQuery and tools such as DBT.
Strong understanding of data modeling, schema design, and building maintainable, modular data systems.
Experience implementing CI/CD best practices for data pipelines, including automated testing, validation, and deployment workflows to ensure reliable and repeatable production releases.
Experience monitoring and operating production data systems, including pipeline observability, data quality checks, and incident response.
Ability to identify performance and cost risks in large-scale data systems and implement optimizations.
Strong collaboration skills and experience working with cross-functional partners including analytics engineers, data scientists, and product teams.
Proven ability to independently execute medium-sized projects and deliver reliable, production-grade systems.

Nice To Haves

Familiarity with Kubernetes-based data infrastructure, including deploying and operating containerized data services and workflows in a production environment.
Experience migrating legacy ETL systems to modern orchestration frameworks.
Familiarity with observability and monitoring tools for data pipelines (e.g., Datadog or similar).
Experience operating data systems with strict SLAs for freshness, reliability, and cost efficiency.

Responsibilities

Design, build, test, and deploy scalable and reliable data pipelines that power analytics and product decision-making.
Own medium-sized data platform initiatives end-to-end, from initial design through production deployment and operational support.
Design, migrate, and maintain data workflows in Apache Airflow, including supporting migration of legacy ETL systems to modern orchestration patterns.
Ensure pipeline reliability by proactively monitoring workflow SLAs for freshness, quality, and performance, and resolving failures efficiently.
Implement and utilize monitoring systems to detect pipeline failures, schema drift, and data quality anomalies.
Participate in on-call rotations and contribute to incident response and root cause analysis for data incidents.
Apply best practices in warehouse performance and cost optimization, including partitioning, indexing, and efficient data modeling to control BigQuery spend.
Build maintainable, modular data models and pipelines using reusable patterns and shared components across the team.
Partner closely with Analytics Engineering, Data Science, and business stakeholders to deliver durable improvements across the ingestion, transformation, modeling, and serving layers of the data platform.
Contribute to operational excellence through documentation, monitoring improvements, and participation in postmortems and reliability initiatives.