Senior Software Engineer - Data
Confirmed on the employer's own hiring board on Oct 20, 2026. First seen by Alion on Oct 20, 2026.
The Mission
Our mission is to ensure that no one in the world has to suffer from a treatable skin disease because of an access issue ever again.
About Honeydew
Honeydew is building a platform for hundreds of millions of people globally to access expertise and science-backed skin treatments, from prescription to retail. Our tech-forward dermatology experience helps people with chronic skin conditions access licensed specialists, FDA-approved treatment, and ongoing support in record time. Honeydew is the future of skin health - digitally native, scientifically rooted, integrated end-to-end, and powered by AI.
The Opportunity
Every feature team and business stakeholder at Honeydew relies on the Platform team for data they can trust. As a Data Engineer, you’ll lead the work that makes our data more secure, reliable, and accurate, from hardening our data layer, to making data easy for teams to reach on their own, to tightening how we protect patient data in a HIPAA environment. You’ll review and test your teammates’ work, and partner with feature teams and other stakeholders to shape what our data platform can do next.
What You'll Build (Your First 6 Months)
A healthy data layer: by day 60, monitoring in place for query performance, data quality, and capacity, with the top five issues it surfaces resolved; by day 180, a documented schema change and data quality process that feature teams follow, and no production incidents caused by data layer changes in the areas you support
Stronger data security: by day 60, at least two data security or governance improvements delivered, such as audit log coverage for PHI access, tighter data store access controls, or automated detection of sensitive data in non-production environments
How You'll Make an Impact
Design and migrate. You'll work with the engineering manager to identify and plan projects, design data models, and execute migrations using agentic engineering methods.
Build reliable data access. You'll build and maintain data pipelines and reporting access, so feature teams and business stakeholders get data they can trust without filing a request.
Protect and enable. You'll review other engineers' code, especially data-related changes, and help other teams with data requests and security reviews.
What We're Looking For
The Non-Negotiables:
Data engineering and architecture - production experience designing relational data models, planning and executing data migrations, and building reliable data pipelines, applying current best practices in schema design, query performance, and data quality; experience with data analytics or warehousing
Object-oriented programming language - production experience building backend services and APIs
Data security and governance - experience securing sensitive data in production, including access control, encryption, audit logging, and handling of PHI or PII, ideally in a regulated environment
The Nice-to-Haves:
Experience in healthcare setting, or another heavily regulated domain
Experience building and using custom agents[MISSING - hiring manager to provide]
Experience with Typescript, AWS, serverless functions, PostgreSQL, Databricks
You'll Thrive Here If…
You make a plan and execute to that plan and timeline.
You identify and report timeline issues early.
You identify and champion opportunities to improve the ecosystem you work in. You deliver on the date you commit to, without cutting quality
You keep up with current best practices, including AI-assisted engineering.
You give direct, useful feedback in code review.
You request and give help when needed.
You're glad to help another team with a data or security request
You write things down so the next engineer can rely on them.
Our tech stack
Languages: TypeScript, Python, SQL
Frontend: React, Redux Toolkit, Material UI, Tailwind CSS, Vite, Next.js
Backend: Node.js on AWS Lambda, GraphQL (AWS AppSync, Apollo), REST (API Gateway), event-driven architecture (EventBridge, SQS, SNS), authentication (Cognito)
Data: PostgreSQL (RDS), DynamoDB, OpenSearch, S3
Infrastructure: AWS (multi-account, Control Tower, CloudFormation, IAM, SSM, CloudWatch, CloudTrail), Terraform, Serverless Framework, Docker, LocalStack, Git, CI/CD on Bitbucket Pipelines and GitHub Actions
Analytics: Databricks, dbt, Amplitude
AI: OpenAI and Anthropic APIs, Cursor
Testing: Vitest, Jest, Playwright