Senior Software Engineer, Data Platform

Role overview A senior engineering role on a Data Platform team that owns the full data stack from ingestion through transformation, governance, and delivery to BI tools. The position focuses on building and operating the distributed services and compute infrastructure behind the platform, with an emphasis on evolving the architecture toward AI-first data consumption. It is a high-ownership software engineering role in the data domain rather than an analytics-modeling position.

Responsibilities

- Design, build, and operate distributed services and compute infrastructure, including containerized workloads on AWS ECS, autoscaling, resource sizing, and the orchestration layer that coordinates them. - Own end-to-end design and development of scalable data pipelines and data-moving services, covering ingestion, orchestration, transformation, and delivery. - Improve how event-sourced data flows from production systems into the data infrastructure and back out to downstream services and tools. - Participate in on-call rotations, owning production issues in distributed data systems from detection through resolution. - Apply secure data handling practices, including PII classification and masking, role-based access controls, least-privilege patterns, encryption, key management, lineage, and audit trails that support SOC 2 and regulatory obligations. - Shape the technical direction of the platform as it evolves toward AI-consumption, designing pipelines and infrastructure for unstructured data access, model workloads, and AI tooling integration. - Build interfaces used by data engineers, analysts, and AI systems, including the paths that move curated data from the warehouse back into production services. - Mentor engineers and analysts, raising the technical bar for testing, code review, and operational readiness across the data stack.

Requirements

- 5+ years of experience building and operating production software systems, with substantial time spent on data pipelines, streaming platforms, or data infrastructure. - Demonstrated ability to design distributed systems, with clear reasoning about concurrency, backpressure, idempotency, partial failure, and associated tradeoffs. - Hands-on experience running containerized workloads in production on AWS, including scaling, resource sizing, and performance and cost tuning under load. - Track record of independently owning complex production systems, supported by automated testing, code review, CI/CD, and infrastructure as code (e.g., Terraform). - Experience with workflow orchestration at scale, such as Airflow or equivalent tools. - Strong motivation to use AI tools and workflow automation as the primary way work gets done.

Nice to have

- Hands-on experience with event-sourced or append-only log systems, change data capture, or streaming platforms like Kafka or Kinesis. - Working knowledge of cloud data warehouse technologies such as Snowflake or equivalent, including concepts like virtual warehouses, scaling, and cost control.

Benefits and work setup

- Remote-first work arrangement. - Comprehensive health, dental, and vision coverage. - Equity participation. - Retirement plan support. - Enhanced mental health support for employees and dependents. - Fertility healthcare and family-forming benefits. - Student loan planning and repayment resources. - Monthly work-from-home stipend and quarterly lifestyle stipend. - Engaging virtual and in-person team-building experiences and offsites.