Senior Data Engineer II

Role Description

An established, well-funded consumer technology marketplace is hiring a Senior Data Engineer II to help own and evolve its company-wide data platform. This is a senior individual-contributor role for an engineer who combines strong architectural judgment with hands-on execution.

You will independently design data pipelines and platform improvements while continuing to write production code, troubleshoot complex issues, and ship alongside the team. The scope spans:

• Ingestion
• Transformation
• Orchestration
• Warehouse infrastructure
• Serving
• Reliability
• Observability
• Access controls
• Developer experience

The data engineering team is intentionally lean and owns the platform and pipeline infrastructure used across the company. Separate teams own downstream analytics modeling and MLOps, so this role is best suited to a core data engineer or data platform engineer rather than an analytics engineer or ML platform specialist.

Initial work will include supporting a major overhaul of revenue and CRM-related systems, designing new integrations across those systems, and building data structures that can support both internal and customer-facing AI capabilities. You will have significant autonomy, visibility, and influence across Engineering, Product, Analytics, and Data Science.

Qualifications

• 7+ years of experience in data engineering or data platform engineering.
• Demonstrated ownership of a broad data platform or major end-to-end platform architecture, not only implementation of individual pipelines or reports.
• Deep production experience with Apache Airflow or a comparable workflow orchestrator.
• Production experience with BigQuery or a comparable cloud data warehouse such as Snowflake, Redshift, or Databricks.
• Strong hands-on Python and SQL skills, including the ability to work through live technical exercises.
• Experience operating containerized data infrastructure in production, including deploying services and diagnosing reliability or scalability issues.
• Experience building or materially improving CI/CD practices for data pipelines, including automated testing, validation, and deployment.
• Experience leading monitoring, observability, and incident response for a data domain or platform.
• A record of independently explaining and defending technical tradeoffs to both technical and non-technical stakeholders.
• Experience influencing decisions across Engineering, Product, Analytics, or Data Science teams.
• Experience mentoring engineers and reviewing data architecture or modeling decisions.

Requirements

• Kubernetes-based data infrastructure.
• Leading a legacy ETL-to-modern-orchestration migration from planning through production rollout.
• Platform-wide monitoring and observability using Datadog or comparable tools.
• Building internal tools or AI-assisted automation used by other engineers.
• Experience in both a startup or lean engineering environment and an established mid-sized technology company.

Benefits

• Own architecture at company-wide scope rather than a narrow slice of a large platform.
• Stay deeply hands-on while influencing long-term platform strategy.
• Lead meaningful greenfield work alongside modernization of existing systems.
• Join a collaborative, low-ego culture that values thoughtful disagreement, continuous learning, and sustainable work-life balance.
• Work remotely within the United States, with opportunities for company or team meetups up to four times per year.

Company Description

The employer is committed to providing equal employment opportunities and considers qualified applicants without regard to legally protected characteristics.