# Senior Software Engineer, Data Processing

**Company:** [Protege](https://hotfix.jobs/companies/protege)
**Location:** Remote
**Role:** Data Engineering
**Experience:** 5+ years
**Skills:** Python, AWS, Distributed Data Processing, Data Pipelines, Airflow, Dagster, GCP, Azure, Machine Learning, NLP
**Posted:** 2026-06-03

> As a Senior Software Engineer, Data Processing, you will own the data processing layer at ingestion, building and operating systems that transform large-scale source data into clean, structured, AI-ready datasets. This is a hands-on, backend- and data-heavy role with end-to-end ownership of data pipelines.

## Job Description

## Ingestion & Processing Systems
* Design, build, and operate the ingestion systems that process large volumes of multimodal data into usable, well-structured datasets
* Own the ingestion path end to end, from how data lands to how it is validated, processed, tracked, and made available downstream
* Build modality-specific processing steps for real-world source data, such as medical imaging processing, audio and video metadata extraction, quality validation, and notes processing
* Build parsers, validators, and normalization logic that can systematically handle messy, non-standard, and high-variance source formats
* Turn repeated one-off data handling work into reusable processing patterns, internal tooling, and platform capabilities

## Scale, Performance & Reliability
* Build for high volume and high throughput, optimizing systems for reliability, cost, and speed
* Work across distributed and parallel compute systems to process workloads that do not fit well on a single machine
* Choose the right execution model for the workload, including batch processing, distributed execution, and modern compute patterns for unstructured data and inference-heavy processing
* Diagnose and resolve bottlenecks across ingestion and processing systems, and keep performance from degrading as volume and modality complexity grow

## Data Quality, Security & Compliance
* Build validation and quality checks that catch bad, incomplete, or malformed data before it propagates downstream
* Handle sensitive and regulated data, including PHI, with the security and care the domain demands, including de-identification where required
* Track provenance, metadata, and usage constraints through the ingestion path so downstream use remains compliant and auditable
* Raise the quality bar for observability, debuggability, and operational reliability across the ingestion layer

## Cross-Functional Partnership
* Partner with product and Data Lab to support new modalities, new partner requirements, and non-standard source data
* Work directly with partner engineering teams when needed to translate source-system realities into robust ingestion and processing design
* Surface recurring patterns that are worth standardizing into reusable transforms, validators, and internal tooling
* Help shape how Protege handles new data types as the platform expands into more complex data environments

## What You Bring

### Must Haves
* 5+ years building and operating production backend or data systems, with real experience in data processing at scale
* Hands-on experience designing and running large-scale data pipelines
* Strong programming skills in Python
* Experience with distributed data processing
* Strong proficiency with AWS
* Comfort with messy, varied, high-volume data and high ambiguity, with a knack for finding patterns in complex environments
* Attention to detail without losing speed, and a bias to action
* Excited to work on a product built around moving and processing large volumes of data
* Curious, tenacious, and proactive

### Nice to Haves
* Experience processing one or more specific modalities at scale: medical imaging (e.g., DICOM), text, audio or video
* Background working with sensitive or regulated data environments (HIPAA, healthcare compliance, PHI handling)
* Experience with streaming systems or workflow orchestration (e.g., Airflow, Dagster)
* Experience with GCP and Azure
* Prior startup experience as a founding or early engineer
* Familiarity with ML, NLP, or LLM-based systems, including embeddings and fine-tuning

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