Staff Data Engineer

robotsandpencils; United States; Remote; USD 130,000 - 190,000; Full time

Job summary

robotsandpencils is seeking a Senior Data Platform Engineer to set technical direction for enterprise data platforms, including lakes, warehouses, and pipelines. The role blends hands-on engineering with architecture decisions, platform modernization, and mentorship for data foundations powering production AI/ML systems.

You will design scalable data architectures, enforce governance and data quality, and automate deployments while collaborating with product, analytics, AI, backend, and platform

Qualifications

• At least seven years of data engineering experience, including leadership of complex platform initiatives.
• Strong distributed systems and architecture background with cloud data platforms, warehouses, orchestration, and data modeling.
• Expert in Python, Scala, and SQL; experience with streaming systems and real-time processing.
• Experience with governance, data quality, container orchestration, and CI/CD for data systems.
• Delivery of production AI/ML data pipelines and experience with AI-assisted coding tools.
• Technical leadership, mentoring, stakeholder communication, and sound judgment in ambiguity.
• Nice to have: data mesh/fabric, lakehouse architectures, or governance framework implementation.

Responsibilities

• Design and build scalable data architectures and batch or streaming pipelines, balancing reliability, security, performance, and cost.
• Establish data quality, governance, compliance, and data contract practices across platforms and teams.
• Develop pipeline monitoring, alerting, deployment automation, and integrations with APIs and external systems.
• Build data flows for AI and ML workloads, including embeddings, vector stores, retrieval-augmented generation, feature stores, and training or inference data.
• Guide platform strategy with technical and business stakeholders, make consequential architecture decisions, and mentor other engineers.
• Set engineering standards and collaborate with product, analytics, AI, backend, and platform teams.

Skills

Python

Scala

SQL

Distributed systems

Data pipelines

Leadership

Data governance

CI/CD

AI/ML pipelines

Cloud data platforms

Job description

Role overview

Set technical direction for enterprise data platforms and lead their design and delivery across lakes, warehouses, and pipelines. The role combines hands‑on engineering with architecture decisions, platform modernization, and mentorship, including data foundations for production AI and machine learning systems.

Responsibilities

• Design and build scalable data architectures and batch or streaming pipelines, balancing reliability, security, performance, and cost.
• Establish data quality, governance, compliance, and data contract practices across platforms and teams.
• Develop pipeline monitoring, alerting, deployment automation, and integrations with APIs and external systems.
• Build data flows for AI and ML workloads, including embeddings, vector stores, retrieval-augmented generation, feature stores, and training or inference data.
• Guide platform strategy with technical and business stakeholders, make consequential architecture decisions, and mentor other engineers.
• Set engineering standards and collaborate with product, analytics, AI, backend, and platform teams.

Requirements

• At least seven years of data engineering experience, including leadership of complex platform initiatives.
• Strong distributed systems and architecture background, with expertise in cloud data platforms, warehouses, orchestration, and data modeling.
• Expert skills in Python, Scala, and SQL; experience with streaming systems and real-time processing.
• Experience with governance, data quality, container orchestration, and CI/CD for data systems.
• Demonstrated delivery of production AI/ML data pipelines and practical expertise with AI‑assisted coding tools.
• Technical leadership, mentoring, stakeholder communication, and sound judgment in ambiguous situations.

Nice to have

• Experience with data mesh or fabric, lakehouse architectures, or governance framework implementation.