Senior Data Platform Engineer

About the role

You will design, implement, and operate a streaming-first, domain-oriented data platform that serves as the central nervous system for analytics and AI across 1Password. You will own the end-to-end lifecycle of data products, from raw events to curated, governed, and self-serve datasets that drive business decisions. You will collaborate closely with product, engineering, and analytics teams to define data strategies, establish standards, and deliver reliable data infrastructure that scales with the company's rapid growth. You will champion data quality, security, and observability while mentoring other engineers and elevating the collective capability of the data platform organization. You will ensure that data is treated as a first-class product, with clear ownership, discoverability, and trust at its core.

Key facts

What you'll do

Architect and maintain a scalable streaming data platform centered on Apache Kafka and event-driven architectures to support real-time use cases.

Design and implement paved paths and self-serve data products that enable analysts, engineers, AI systems, and business users to access trusted data with minimal friction.

Establish and enforce schema governance, data quality rules, and lifecycle management practices to ensure consistency, reliability, and compliance across the data estate.

Lead the design and operation of a secure, governed data medallion architecture that balances performance, cost, and usability for diverse workloads.

Partner with cross-functional teams to translate business requirements into robust data models, pipelines, and analytical datasets that support critical initiatives.

Mentor and guide junior and mid-level data engineers by providing technical leadership, code reviews, and knowledge-sharing sessions that strengthen the platform team.

Evaluate, prototype, and integrate emerging AI technologies and tools to enhance data platform capabilities, automation, and developer experience.

Collaborate with security and infrastructure teams to implement least-privilege access controls, encryption, and auditing across data pipelines and storage layers.

Own key reliability, performance, and scalability metrics for the data platform, driving improvements through automation, monitoring, and alerting.

Contribute to open source and internal tooling ecosystems, ensuring that data platform components are maintainable, observable, and aligned with industry best practices.

Requirements

You bring 7 or more years of proven experience designing, building, and operating production-grade data platforms and distributed systems.

You hold a Bachelor of Science, Master of Science, or Ph.D. in Computer Science or a related technical field with 7 or more years of relevant industry experience.

You have 5 or more years of hands-on experience with cloud platforms such as AWS or GCP, including core services that support data infrastructure and operations.

You have 5 or more years of experience with big data query engines and data stores such as Databricks, Snowflake, Redshift, or similar platforms.

You demonstrate deep expertise in streaming architectures and event-driven data pipelines, including real-world deployment and operations.

You have 5 or more years of experience with infrastructure as code tools such as Terraform or Ansible to provision and manage cloud resources reliably.

You have 5 or more years of experience building, deploying, and operating large-scale, distributed systems with a strong grasp of scaling, replication, consistency, and high availability.

You communicate effectively and work cross-functionally with engineers, analysts, product managers, and designers to prioritize needs and deliver impactful data solutions.

You prefer a background in computer science with a strong foundation in data systems, distributed computing, and database internals.

Nice to have

Experience implementing secure systems and data stores with least-privilege access controls and encryption at rest and in transit.

Hands-on experience with streaming technologies such as Apache Kafka, Kinesis, Flink, or similar platforms.

Experience building quality monitoring, alerting, and observability solutions using tools like Prometheus, Grafana, or Datadog.

Experience with orchestration tools such as Dagster or similar frameworks for pipeline management and scheduling.

Experience with data modeling and transformation tools such as dbt to ensure maintainable and well-structured analytics layers.

Practical notes

This is a remote opportunity within Canada and the United States.

What you can expect

Design, implement, and maintain a streaming-first data platform built on Apache Kafka, Protobuf, and a modern medallion architecture to enable trusted, real-time data access.