Senior Data Engineer II - Data Platform

Remote within the United States | Full-time
Total target compensation: $145,000-$207,000, including a base salary of $130,000-182,000, depending on geographic zone, experience, and qualifications, plus equity

About the opportunity

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, and 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.

What you will do

• Own and evolve data architecture across ingestion, transformation, orchestration, warehouse, and serving layers.
• Make independent architectural decisions and clearly evaluate tradeoffs involving freshness, cost, scalability, reliability, and simplicity.
• Design and implement platform-level improvements involving warehouse cost management, compute efficiency, access controls, reliability, and developer experience.
• Identify and lead long-term platform investments before orchestration, CI/CD, data access, testing, or observability gaps become blockers.
• Drive large, technically complex projects, including initiatives that span multiple teams and business functions.
• Build and improve monitoring, alerting, automated testing, and incident-response practices for data pipelines and infrastructure.
• Deploy, maintain, and troubleshoot containerized data services in production.
• Partner directly with Engineering, Product, Analytics, and Data Science stakeholders on technical decisions that affect their roadmaps.
• Mentor data and analytics engineers, review architectural decisions, and serve as a trusted technical peer across teams.
• Build practical AI-assisted tooling and automation that improves the broader team's engineering workflow while maintaining strong validation standards.

What you will need

• 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.

Preferred experience

• 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.

What makes this role compelling

• 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.

Candidates must be authorized to work in the United States without current or future employer sponsorship. The employer is unable to provide visa sponsorship or transfer support for this position.

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