Data Engineer, Product

Our Data Team is the backbone of informed decision-making and strategic planning at FuturHealth. We collaborate with all stakeholders across the organization, including Finance, Customer Experience, Marketing, Product, and more. We don't view data as just "tickets to fulfill" or "pipelines to maintain." We own the data ecosystem end-to-end and our mission is to ensure data is obsessively accurate, deeply understood, and seamlessly integrated into day-to-day operations to drive real business growth.

About the Role

This is a hybrid, high-ownership role for an engineer who cares deeply about both the code and the context. You are equally comfortable building an ingestion pipeline, modeling data in dbt, and sitting down with stakeholders to figure out the why behind a metric.

In the age of AI, we care far more about your problem-solving speed, data intuition, and bias for action than whether you have memorized specific syntax. If you are a seasoned engineer who moves fast, learns relentlessly on the fly, and takes extreme ownership over outcomes, you will thrive here.

You will own data end-to-end: ingesting and orchestrating raw data, modeling it in dbt, exposing it through self-service BI, and ensuring our business stakeholders are empowered with trustworthy data.

What You Will Do:

• Design, build, and maintain reliable ETL/ELT pipelines.
• Own the dbt models and datasets that serve as the source of truth for key business metrics.
• Govern the semantic layer so metric definitions stay consistent and documented across reporting surfaces.
• Implement testing and observability for analytics pipelines, and bring in tooling that strengthens the stack.
• Partner with analysts and stakeholders to gather requirements and turn them into clean, well-scoped solutions.
• Run hands-on analysis and deep dives: KPIs, release performance, and A/B tests.
• Take end-to-end ownership: jump into unfamiliar codebases, data sources, and business domains and figure out how to ship reliable, scalable solutions.
• Own problems from scoping through production, ramping fast on unfamiliar codebases, data sources, and domains
• Leverage AI tools and agentic workflows (OpenCode, Claude Code, Zed, and Eve) to write code and debug faster, while helping build natural-language querying tools/agents for non-technical stakeholders.

It’s a Perfect Match If You Have:

• 3–5+ years as an analytics engineer, data engineer, or similar role.
• Strong knowledge of SQL, Python, dbt, and modern data modeling best practices.
• Comfortable with large-scale data systems (BigQuery, Snowflake, etc.).
• Strong familiarity with CI/CD, Git-based workflows, and automated testing.
• Proficiency in data analysis and BI tools (Looker, Tableau, Power BI).
• Adaptable and fast-learning, with a strong bias for ownership and a habit of leveraging AI to work faster.
• Statistics and experiment analysis (A/B testing, segmentation, metric definition).
• Experience leveraging AI to accelerate coding lifecycle from idea to product and enabling AI-driven analytics for stakeholders
• Clear communication and real stakeholder instincts.

Preferred Qualifications:

• Experience with our stack: GCP, and building/maintaining Cloud Composer / Airflow DAGs.
• LookML and semantic-layer / metric-governance experience.
• Subscriptions experience (retention, rebill/refill, LTV).
• Cloud-native pipelines (Cloud Functions, GCP Logging/Monitoring) and data-job alerting.
• Healthcare, telehealth, or MSO experience.
• Fast-paced or startup environments.