Solutions Engineer, Healthcare

Company Overview We are building Protege to solve the biggest unmet need in AI — getting access to the right training data. The process today is time intensive, incredibly expensive, and often ends in failure. The Protege platform facilitates the secure, efficient, and privacy‑centric exchange of AI training data. Solving AI’s data problem is a generational opportunity. We’re backed by world‑class investors and already powering partnerships with some of the most ambitious teams in AI. The company that succeeds will be one of the largest in AI — and in tech. We’re a lean, fast‑moving, high‑trust team of builders who are obsessed with velocity and impact. Our culture is built for people who thrive on ambiguity, own outcomes, and want to shape the future of data and AI. Role Overview We're hiring a Solutions Engineer for our Healthcare vertical to operate and improve the infrastructure that moves large‑scale healthcare datasets between partners, cloud platforms, and customers. This is a hands‑on delivery engineering role focused on production execution, operational reliability, and safe data movement – not a pre‑sales solutions role. You will work across AWS, GCP, Azure, Snowflake, and Protege's internal platform to run cross‑cloud transfers, lightweight transformations, packaging, and validation for healthcare data deliveries. You will manage real production workflows, debug failures, and keep data integrity, auditability, and repeatability high. At its core, this role is about making complex healthcare data delivery reliable at scale: taking messy, multi‑system workflows and turning them into safe, repeatable operations that support customers, partners, and internal teams. What You'll Do Own cross‑cloud data movement and delivery Execute and monitor large‑scale data transfers across AWS S3, Google Cloud Storage, Azure Blob, Snowflake, and customer environments. Use CLI tools such as rclone, s5cmd, and cloud‑native utilities to move data safely and efficiently. Manage credentials, permissions, manifests, and delivery packaging artifacts required for ingestion, subset delivery, and handoff workflows. Build structured data assembly and lightweight transformation workflows Use Python and SQL to join datasets, add derived columns, clean data, and validate CSV, Parquet, and database tables. Support customer‑specific assembly work that turns raw inputs into delivery‑ready datasets. Apply a high bar for data integrity, structure, and reproducibility before handoff. Operate internal pipelines with production discipline Leverage Protege's Dagster‑based platform to orchestrate data processing and delivery. Maintain clean separation between pre‑production and production workflows and validate configs before runs. Build lightweight scripts and command‑line workflows for filtering, manifest generation, validation, and recovery. Create leverage for the team and platform Document steps, outputs, and recovery paths for auditability and repeatability. Identify recurring delivery patterns, failure modes, and manual toil. Partner with Engineering to turn one‑off operational work into repeatable platform capabilities and test new tooling before go‑live. What Success Looks Like 30 days: Learn the environments and delivery motion. Build working knowledge of partner and customer delivery patterns, environments, permissions models, and core tooling. Shadow live deliveries and get operational on standard validation and rerun workflows. 60 days: Own scoped production work. Run scoped cross‑cloud deliveries and light transformations with limited support. Improve runbooks, validation steps, and status communication so work is safer and easier to repeat. 90 days: Create leverage and reliability. Independently own complex delivery workflows across multiple systems while reducing rework and surfacing platform improvements that eliminate operational risk and manual toil. What You Bring Strong hands‑on experience with data pipelines, both orchestrated and manual, in real production environments. Fluency with command‑line tooling in Linux or MacOS and strong scripting ability in Python, SQL, and Bash/shell. Experience working with cloud storage systems and large‑scale cross‑cloud data movement. High bar for data integrity, validation, reproducibility, and auditability – especially for regulated data. Calm, methodical debugging instincts and strong operational judgment about when to rerun, recover, or escalate. Ability to manage multiple delivery workflows simultaneously while collaborating well with a distributed team. Bonus if you have experience with AWS S3, GCS, Azure Blob, Snowflake, IAM debugging, Dagster/Airflow, healthcare data, or AI training data. #J-18808-Ljbffr