Staff Data Engineer

Remote (United States)

Job Details

Location: United States

Workplace: Remote

Employment Type: Full-time, Permanent, Exempt

Experience: 5+ years of data engineering experience building and operating production data pipelines and platforms

Core Areas: Data Platform Engineering, Spark, Kafka, Iceberg, Airflow/MWAA, Data Lake Architecture, Data Governance, Data Quality

Compensation: $186,500–$255,000 per year plus bonus eligibility and equity

About the Role

This opportunity is for a Staff Data Engineer to take ownership of major Data Platform Engineering initiatives spanning the data lake, production pipelines, event instrumentation, data quality, data governance, and cloud data infrastructure. The role designs and operates scalable batch, streaming, and real-time data systems while helping evolve ingestion, processing, storage, and serving patterns across the platform.

The position works extensively with Spark, Kafka, Iceberg, Airflow/MWAA, Druid, and EMR on EKS, with a strong focus on reliability, observability, governance, and privacy. Responsibilities also include GDPR and CCPA data controls, AI-assisted development workflows, production troubleshooting, technical leadership, and mentoring engineers.

What You'll Do

• Design data solutions for batch, streaming, and real-time workloads.
• Design and build reliable production data pipelines using Spark, Kafka, Iceberg, and Airflow/MWAA.
• Advance data lake and data platform architecture across ingestion, processing, storage, and serving patterns.
• Build, maintain, and scale cloud data infrastructure using Kafka, Airflow, Druid, and EMR on EKS with reliability and observability as core design requirements.
• Implement and continuously improve data quality, observability, reliability, and governance across production data pipelines and systems.
• Define and improve event instrumentation and governance standards, including event schemas, validation, and schema evolution.
• Build centralized platform capabilities for PII handling, data retention, deletion, and residency to support GDPR and CCPA requirements.
• Develop agent harness capabilities that encode data engineering standards, pipeline patterns, and quality gates into AI tools so agents can safely generate, review, and operate on data platform code.
• Contribute hands-on to complex technical initiatives and troubleshoot production data platform issues.
• Mentor engineers and collaborate with Analytics Engineering and other technical teams on complex data and platform challenges.
• Embed data engineering standards, pipeline patterns, and quality gates into AI-assisted development workflows.

Qualifications

Required Experience

• 5+ years of data engineering experience building and operating production data pipelines and data platforms.
• Strong experience with Spark and distributed data processing, including designing and optimizing production data pipelines.
• Experience designing batch, real-time, and streaming data solutions using technologies such as Kafka, Spark Structured Streaming, and change data capture (CDC).
• Experience implementing data quality, observability, and reliability practices across production data systems.
• Experience with cloud data infrastructure, CI/CD, and infrastructure-as-code.
• Demonstrated experience leading complex data engineering initiatives end to end, from technical design and decomposition through implementation and production.

Required Skills

• Strong programming and SQL skills with the ability to build reliable, maintainable production data systems.
• Ability to navigate ambiguous technical problems, evaluate architectural tradeoffs, and make pragmatic engineering decisions.
• Ability to design beyond individual pipelines and consider the full data ecosystem from upstream data producers through downstream consumers.
• Experience with data governance and privacy practices, including PII management, retention, deletion, data residency, GDPR, or CCPA.
• Experience with event instrumentation and governance, including event schemas, validation, and schema evolution.
• Ability to collaborate with engineers on complex technical problems and establish practical engineering best practices.
• Experience applying AI and emerging technologies in development workflows and helping technical teams adopt new tools through shared practices and learnings.

Education

• BA/BS degree or equivalent professional experience.

Benefits

• Equity through restricted stock units (RSUs).
• Comprehensive medical, dental, and vision coverage for full-time employees and their dependents, with most premiums covered.
• 12 weeks of paid parental leave for all parents, plus 6 or more additional weeks of paid leave for birthing parents.
• Inclusive support for family planning, menopause, and midlife transitions.
• Flexible vacation, paid holidays, and a sabbatical program.
• Mental health resources, therapy, and coaching.
• 401(k) with a 100% employer match of up to $6,000 per year in the United States.
• Monthly localized stipends supporting work and wellness expenses, including Wi-Fi and fitness.
• Eligibility for an annual company bonus program for full-time, permanent, non-commission employees.