Senior Software Engineer, Data Platform

Rheaction • Los Angeles, CA

1d • Remote

About The Position

We're looking for a Senior Software Engineer, Data Platform to build and own a data platform from the ground up. This is a highly autonomous, zero-to-one role focused on turning large volumes of complex, real-world data into clean, consistent, and reliable datasets for machine learning and product teams. You'll architect high-throughput pipelines, establish data quality and reproducibility standards, and build the infrastructure and tooling needed to make data easy to discover, process, label, and use.

Requirements

• 5–8 years of software or data engineering experience.
• Proven ability to architect a data platform or high-throughput pipeline from a blank page.
• Strong Python and data tooling experience, such as PyArrow, Polars, Pandas, NumPy, or SciPy.
• Experience with AWS or another major cloud platform and deploying production data pipelines.
• Experience with a services language such as Go, Rust, or TypeScript.
• Strong understanding of data lineage, versioning, reproducibility, and data quality.
• Experience building backfill and reprocessing systems.
• High ownership and ability to operate independently in a startup environment.
• U.S. citizenship required.

Nice To Haves

• Founding or early data-platform engineering experience.
• Experience with sensor, audio, video, telemetry, or geospatial data.
• Airflow, Prefect, MLflow, W&B, or similar tooling.
• Experience building labeling tools or ML data workflows.
• Strong full-stack/generalist capabilities.
• Experience treating datasets as a product, with strong documentation and discoverability.

Responsibilities

• Architect and build high-throughput data pipelines from scratch.
• Develop pipelines for processing, aligning, transforming, and calibrating complex datasets.
• Build backfill and reprocessing frameworks for historical data.
• Establish dataset lineage, versioning, and reproducibility.
• Build APIs and tooling for dataset discovery and access.
• Develop data-quality metrics, monitoring, and alerting.
• Optimize storage, compression, partitioning, and data access patterns.
• Build internal tools and workflows for ML and data-labeling teams.
• Partner closely with ML engineers to understand and support their data requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed