Data Engineer

Data Engineer

Location: San Francisco, California, United States

Industry: Software Development

Work Setting: Remote

Are you passionate about building modern data platforms, creating scalable data pipelines, and enabling data-driven decision-making through reliable, secure, and intelligent data systems? We are seeking a Data Engineer to own and evolve the organization's data platform, ensuring high-quality, accessible, and well-governed data that powers analytics, business operations, and AI-driven innovation.

In this role, you will be responsible for designing, building, and maintaining end-to-end data infrastructure, including data ingestion, transformation, orchestration, warehousing, governance, and automation. You will work closely with engineering, product, operations, and business teams to create scalable solutions that unlock the full value of organizational data.

Key Responsibilities

Data Platform Engineering

• Own and manage the end-to-end enterprise data platform, ensuring reliability, scalability, security, and performance.
• Design and maintain data architectures that support analytics, reporting, operational workflows, and AI initiatives.
• Continuously improve platform capabilities, operational efficiency, and data accessibility.
• Develop scalable systems that support evolving business requirements.

Data Pipeline Development

• Build, optimize, and maintain robust data ingestion and transformation pipelines.
• Design ETL/ELT processes that move data efficiently across platforms and systems.
• Ensure data pipelines are reliable, observable, and capable of handling growing data volumes.
• Automate data movement and processing workflows using modern engineering practices.

Data Warehousing & Modeling

• Manage and optimize cloud data warehouse environments.
• Design and maintain scalable data models that support analytics, reporting, and self-service business intelligence.
• Establish data structures that improve usability, performance, and consistency.
• Partner with stakeholders to define metrics, dimensions, and business reporting standards.

Data Quality & Governance

• Implement data quality frameworks, validation processes, and monitoring solutions.
• Establish governance controls that ensure accuracy, consistency, and trust in organizational data.
• Manage data access controls, permissions, and compliance requirements.
• Support data stewardship and governance initiatives across the organization.

Orchestration & Automation

• Develop and maintain orchestration frameworks that automate data workflows and dependencies.
• Ensure efficient scheduling, monitoring, and recovery mechanisms for data processes.
• Build operational tooling that improves visibility and platform observability.
• Enhance platform reliability through automation and engineering best practices.

AI & Intelligent Data Solutions

• Support the integration of AI and machine learning capabilities within the data ecosystem.
• Explore opportunities to automate workflows and enhance operations using AI-driven solutions.
• Collaborate on initiatives that leverage enterprise data to power intelligent applications.
• Contribute to innovative approaches that improve data accessibility and decision-making.

Infrastructure & DevOps

• Implement Infrastructure-as-Code and modern deployment practices.
• Support cloud-native architectures, containerized environments, and scalable infrastructure solutions.
• Establish CI/CD pipelines and deployment automation for data platforms.
• Collaborate with platform and infrastructure teams to ensure operational excellence.

Cross-Functional Collaboration

• Partner with engineering, product, analytics, operations, and business teams to understand data needs.
• Translate business requirements into scalable technical solutions.
• Provide technical guidance on data architecture, governance, and platform strategy.
• Support organization-wide initiatives that rely on trusted and accessible data.

Required Qualifications

• 5+ years of experience building and maintaining production-grade data platforms.
• Strong expertise in:
• Snowflake
• dbt
• SQL
• Python
• Experience designing and maintaining modern data pipelines and data architectures.
• Strong understanding of data modeling principles and best practices.
• Experience with workflow orchestration tools such as:
• Airflow
• Dagster
• Similar orchestration platforms
• Experience implementing data governance and access management controls.
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder collaboration abilities.

Key Technical Skills

• Snowflake
• dbt
• SQL
• Python
• ETL / ELT Development
• Data Pipelines
• Data Modeling
• Data Governance
• Data Warehousing
• Data Quality Management
• Airflow
• Dagster
• AWS
• Terraform
• Kubernetes
• CI/CD
• Infrastructure as Code
• Workflow Automation
• Data Architecture
• Analytics Engineering

Preferred Qualifications

• Experience supporting AI, machine learning, or intelligent automation initiatives.
• Hands-on experience building cloud-native data platforms.
• Experience implementing data observability and monitoring frameworks.
• Knowledge of modern analytics engineering practices.
• Experience supporting enterprise-scale governance and security initiatives.
• Background working within fast-growing technology-focused organizations.