Senior Data Engineer

Ask any employee to explain their benefits, and you'll likely get a confused shrug. Ask any HR leader if their benefits investment is working, and you'll get an honest "I don't know." For employees, benefits are confusing and overwhelming. For HR and finance leaders, benefits are the second-largest expense, but they lack the visibility to know what's working and what's wasted.

At Avante, we're changing that. We're the first AI-native benefits platform built to turn benefits complexity into clarity. For employees, Avante acts like a personal guide, making benefits simple to understand and use. For leaders, Avante unifies fragmented data and delivers real-time insights so they can improve programs, control costs, and prove ROI.

We're based in Seattle and work 4 days a week in the office (one day remote), we're growing quickly, and are looking for a data engineer who can build and maintain the pipelines and infrastructure that power our AI systems and internal analysis. You'll work across the full data lifecycle - from ingestion and transformation to model deployment and monitoring - while using modern AI-assisted development tools to accelerate your work. This is a hands-on role for someone who cares about building reliable, well-documented systems and wants to work at the intersection of data engineering, and applied AI.

- Own and design DBT models in Snowflake with proper testing and documentation

- Build and architect Python data pipelines that are reliable, testable, and observable

- Orchestrate workflows using Argo-Workflows running on Kubernetes

- Transform raw source data into clean, well-modeled datasets for analysts and AI systems

- Manage and improve scalable Azure cloud data infrastructure

- Work on RAG pipelines and vector stores for unstructured data sources

- Stay current with developments in applied AI and bring relevant innovations to the team

- Use tools like Claude Code and Cursor as part of your daily workflow

- Help establish team practices for effective human-AI collaboration in engineering

- Contribute to adjacent full stack and platform engineering projects

- 5+ years experience building scalable data pipelines and warehouses

- Extensive experience with SQL based transforms in DBT or similar technologies

- Experience operating as a technical lead on a data team

- A passion for data quality and careful testing and validation

- Fluency in python with an emphasis on readable, maintainable code

- Familiarity with Terraform and K8s container orchestration

- Ability to communicate technical concepts clearly to diverse audiences, including internal and external stakeholders

- Flexibility and willingness to wear many hats and help out wherever it is needed

- Early stage startup experience

- Analysis or analytics engineering experience

- Full stack development experience

- Hands-on MLOps experience (model training pipelines, deployment, monitoring)

- Experience with AI prompt engineering and LLM application development

- Experience managing cloud infra such as Kubernetes clusters

- Familiarity with clinical and medical insurance claims data

- DBT for transformation workflows and data modeling

- Snowflake for data warehousing

- Python for data loading, tokenization, and manipulation (Pandas, Polars) and integration with our AI platform

- Data orchestration using Argo-Workflow DAGs

- Azure cloud infrastructure with Kubernetes

- Beat Yesterday – Continuous improvement, innovation, and growth

- Embrace Type 2 Fun – Resilience and positivity in the face of challenges

- Act Like an Owner – Initiative, accountability, and focus on outcomes

- Stay Hungry, Stay Curious – Humility, curiosity, and bold thinking

**Interview Process:**

1. Initial screen with our recruiters

2. Phone screen with the hiring manager

3. On-Site Interview Loop

1. Technical interview focused on real-world problem solving (no whiteboard algorithms!)

2. Behavioral interview to explore your approach to teamwork and challenges

3. 1:1 with our PM

4. Final loop with our Head of Engineering

4. Final conversation with our CEO

If you're excited about solving complex data challenges in the benefits space while making a meaningful impact on businesses and their employees, we'd love to hear from you!