Senior Data Software Engineer
Location: Remote – US
Company Stage of Funding: Profitable, Employee-Owned Growth-Stage Company
Office Type: Fully Remote
Salary: $140,000–$180,000 Base + 10–20% Bonus
Company Description
We’re representing a profitable, high-growth healthcare technology company building the data infrastructure behind some of the world's most sophisticated healthcare supply chains.
The company's platform helps healthcare organizations streamline procurement, manage complex item and product data, and improve operational efficiency across their supply chains. Its technology is already used by 17 of Gartner's Top 25 healthcare supply chains, with customers and partners spanning health systems, hospitals, consulting firms, regulatory agencies, and industry groups.
Unlike many growth-stage technology companies, the business has been profitable since its first year and remains employee-owned. The company doubled in 2025 and is positioned for continued significant growth in 2026.
The team is fully distributed across the U.S. and combines the autonomy of an early-stage company with an established enterprise customer base and profitable business model.
What You Will Do
- Design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, and deliver large healthcare supply-chain datasets.
- Work with complex, messy, and often unstructured source data and transform it into reliable, production-quality data assets.
- Build the underlying data infrastructure powering the company's product, analytics, and customer-facing solutions.
- Architect data solutions on AWS, selecting appropriate technologies and patterns based on performance, reliability, and cost.
- Work with technologies such as S3, Glue, Athena, Python, SQL, and modern orchestration frameworks.
- Develop and improve data-quality systems covering validation, reconciliation, and monitoring throughout the pipeline lifecycle.
- Design scalable data models, schemas, and data-processing patterns.
- Build new integrations and ingestion processes across heterogeneous data sources.
- Partner closely with Product Management, Application Engineering, and Data Operations to translate business and product requirements into technical solutions.
- Prototype and iterate on tools that allow domain experts to review and validate data.
- Participate in architecture discussions, technical planning, and code reviews.
- Improve engineering standards around testing, documentation, maintainability, and code quality.
- Mentor junior engineers and serve as a technical resource across the data organization.
- Use modern AI-assisted development tools as part of the engineering workflow.
Ideal Candidate Background
- 6+ years of software or data engineering experience, including at least 4+ years focused specifically on data engineering.
- Strong production experience with Python and SQL.
- Deep experience designing, building, and maintaining ETL/ELT pipelines at scale.
- Hands-on experience architecting and operating data infrastructure on AWS.
- Experience with AWS data technologies such as S3, Glue, and Athena or comparable services.
- Experience with workflow orchestration frameworks such as Airflow, Dagster, or similar tools.
- Strong understanding of data modeling, schema design, and data warehouse architecture.
- Experience transforming messy or inconsistent source data into reliable, structured datasets.
- Strong software engineering fundamentals rather than experience limited primarily to analytics or BI.
- Comfortable owning technical problems independently and making architectural decisions.
- Strong communication skills and able to work effectively across Engineering, Product, Data Operations, and other technical and non-technical teams.
- Comfortable contributing to code reviews, architectural discussions, and technical planning.
- Experience using AI coding tools such as Claude Code or similar tools for agentic software development.
- Comfortable operating as a high-impact individual contributor while mentoring and supporting other engineers.
Preferred
- Experience in healthcare technology, healthcare supply chain, or enterprise data platforms.
- Experience with data quality, governance, master data management, or similar complex data challenges.
- Experience integrating LLMs or ML models into production data pipelines.
- Understanding of prompt design, evaluation, structured outputs, observability, and managing the cost, latency, and reliability of production LLM systems.
- Experience with NLP, information extraction, entity resolution, or record linkage.
- Experience with embeddings, retrieval, or RAG systems.
- Experience with web scraping or large-scale ingestion from heterogeneous sources.
- Experience writing high-performance data-processing systems in Rust.
- DevOps experience including CI/CD, infrastructure-as-code, containerization, and production workload operations.
- Experience building data platforms serving enterprise customers.
- Bachelor's degree in Computer Science, Engineering, or a related technical discipline.
Compensation and Benefits
- Base salary: $140,000–$180,000.
- Performance bonus: 10–20% of base salary.
- Fully remote within the United States.
- Annual in-person company event.
- 100% covered health, dental, and vision insurance.
- 401(k) matching.
- Flexible PTO.
- Employee-owned company that has been profitable since its first year.
- High-autonomy environment where engineers are expected to own their technical domain.
- Reports to the Lead Data Engineer.
- The first 90 days are expected to progress from learning the architecture and datasets to independently delivering integrations and ultimately shipping a meaningful improvement to data quality, reliability, or throughput.
- Longer term, this engineer will have the opportunity to influence the evolution and technical strategy of the broader data platform while mentoring other members of the team.