Full-Stack Data Engineer (FDE) - Video Vertical

About the Role

Protege is hiring an FDE to support our video vertical. This is an engineering role with two key mandates: own the technical success of our video customers, and build the reusable pipes that enable future scale. You will partner closely with the GM of the vertical, product and platform engineering, Data Lab, and commercial teams to navigate external requirements and develop solutions to execute on those requirements. Protege’s video catalog already possesses hundreds of thousands of hours of raw video, this role will be instrumental in continuing to iterate on ways we curate, process, and deliver often bespoke and highly specific datasets to our customers.

This role is ideal for engineers who prioritize both constantly learning through solving difficult problems and interfacing and directly owning customer outcomes. You'll be responsible for managing customer requests, newly ingested partner datasets and architecture decisions all at the same time, often across multiple deals. The pace is fast, the ambiguity is real, and when deals are live, availability outside standard hours is part of the job. If this type of environment and ownership excites you, there could be a strong fit.

What You'll Do

Own Customer Engagements End to End

• Work with the GM of the video vertical, core technical teams, and commercial stakeholders from feasibility through post-delivery support.
• Translate a customer's model-development goals into an executable technical plan with clear acceptance criteria.
• Own the implementation and operation of the engagements you lead.

Build the Measurement Layer

• Build the tooling that turns raw footage into something we can describe and sell. Partner metadata is usually thin and inconsistent, so most of what we know about a dataset is what we measured ourselves.
• Our existing catalog is far too large for any one person to watch and curate from themselves. This role will continue to expand the ways in which we respond to volume requests on new and unique data requests.
• Iterate on solutions for rapidly characterizing and quality checking unknown datasets to continuously expand our catalog offerings.

Turn Deal Work Into Product Leverage

• Work in tandem with the product and core engineering teams to identify areas of growth for the platform based on customer requests and learnings from the front lines.
• Identify recurring patterns that should become shared cross-vertical platform capabilities and contribute directly to designing and building them.
• Build the audio vertical's technical playbooks and quality standards so the function scales with every new request.

What Success Looks Like

30 Days: Learn and Ship

• Learn Protege's platform, our existing video catalog, active customer and partner portfolio, and current processing and delivery systems.
• Pair with an engineer on a live video deal to understand what a delivery actually looks like here.
• Map the largest gaps in the vertical's tooling and operating model, and propose a prioritized plan for closing them.

60 Days: Own End-to-End

• Operate and run an active video deal as the primary FDE.
• Build or meaningfully extend a tool or workflow that came out of a live customer request.
• Establish a communication cycle with other FDEs and product to surface patterns worth generalizing.

90 Days: Operate Independently

• Serve as the default technical owner across the video vertical's active portfolio, including multiple concurrent deals.
• Own end-to-end architecture and delivery for video customers, including post-delivery support and iteration.
• Establish the first version of the video FDE playbook and reusable toolkit.
• Maintain a concrete roadmap of platform investments aimed at increasing the vertical's delivery capacity.

What You Bring

Must Haves

• 3+ years of experience as an engineer, including meaningful exposure to customers or external technical stakeholders.
• Experience working directly with media data, with a strong preference for video specifically.
• Experience building and operating systems that process, analyze, or deliver data at scale.
• Customer-facing ability, including translating ambiguous requirements, communicating trade-offs, and building trust with technical stakeholders.
• Demonstrated end-to-end ownership, from initial problem definition through implementation, validation, and support.
• High ambiguity tolerance and bias to action, with the judgment to know when to investigate further or push back.
• Comfort with the intensity and pace of a fast-moving environment, including multiple concurrent priorities and time-sensitive customer work.

Nice to Haves

• Hands-on experience with video processing at scale: codecs and transcoding, ffmpeg, shot detection, frame sampling strategies, or perceptual quality measurement.
• Experience at an early-stage company, as a founding or early engineer, or in another startup-like environment with broad ownership.
• Hands-on experience with Python and SQL.
• Experience with search, vector embeddings, semantic retrieval, or ML-assisted data curation.
• Experience evaluating or deploying vision-language models, including building the evaluation harness rather than just calling the model.
• Product engineering experience or a strong product mindset developed in close partnership with users.
• Experience with our cloud and data infrastructure (AWS, Databricks, Dagster, Vercel are the key tools).