Data Engineer (Senior)

About Prospera AI

We're building Sophie, a multi-agent AI orchestrator that helps wealth management advisors deliver more personalized, effective service to their clients. Our platform analyzes behavioral patterns, communication preferences, and emotional states to transform how advisors understand and serve their clients. We're a small, well-funded team at an exciting inflection point — our technology works, customers love the product, and now we're building the engineering team to scale.

About Prospera AI

We're building Sophie, a multi-agent AI orchestrator that helps wealth management advisors deliver more personalized, effective service to their clients. Our platform analyzes behavioral patterns, communication preferences, and emotional states to transform how advisors understand and serve their clients. We're a small, well-funded team at an exciting inflection point — our technology works, customers love the product, and now we're building the engineering team to scale.

The Role

We're looking for a Senior Data Engineer to architect and build our data infrastructure from scratch. You'll create the foundation that powers everything from analytics to ML model training — data warehouse, ETL pipelines, feature stores, and the governance that makes it all maintainable. This is a senior role because we need someone who can design and build with minimal guidance. There's no existing data team to learn from — you're building the platform that everything else depends on.

What You'll Do

Data Infrastructure Architecture

• Design and implement the foundational data infrastructure from scratch
• Set up Snowflake with proper environments, security, and access controls
• Create architectural patterns that scale with the company

ETL/ELT Pipeline Development

• Build robust pipelines from source systems to the data warehouse
• Implement transformations with dbt and orchestrate with Airflow/Dagster
• Integrate Fivetran connectors and custom extraction from Supabase

Data Modeling

• Design dimensional models supporting both analytics and ML use cases
• Create semantic layers that make data accessible to stakeholders
• Implement slowly changing dimensions and proper data governance

ML Data Pipelines

• Build infrastructure feeding Sophie's machine learning capabilities
• Create feature stores for real-time feature serving
• Implement data versioning for reproducibility

What We're Looking For

Must Have

• 5+ years experience with modern cloud data warehouses (Snowflake strongly preferred)
• Extensive ETL/ELT pipeline development with strong SQL skills
• dbt experience required; Airflow, Dagster, or Prefect for orchestration
• Strong Python for data engineering tasks
• AWS experience (S3, Glue, Athena, Redshift)

Great to Have

• ML pipeline experience (MLflow, Feast, feature stores)
• Fivetran or similar managed ELT tools
• Dimensional modeling expertise (Kimball methodology)
• Startup experience building data infrastructure from scratch
• Big data at scale (Spark, distributed computing)

How You Work

• Architectural thinker who balances immediate needs with long-term maintainability
• Self-directed and comfortable with high autonomy
• Strong communicator who can translate technical concepts for stakeholders
• Pragmatic about tradeoffs — knows when to build for scale vs. good enough

What This Role Is Not

• Not a Data Analyst role — you build infrastructure that enables analysis
• Not a Data Scientist role — you build ML pipelines; they build models
• Not a Backend Engineer role — you own the data layer, not the application layer

Compensation & Benefits

• BaseCompetitive — Based on experience and location
• EquityMeaningful early-stage grant with 4-year vesting
• EquipmentProfessional laptop provided + remote work stipend after 6 months
• Time OffFlexible PTO with minimum 15 days encouraged
• LearningAnnual professional development budget
• ScheduleFlexible hours with 3–4 hours daily overlap Americas timezones

Interview Process

1. 1 Resume Review— 1–2 day turnaround
2. 2 Technical Screen— 60 min video conversation with CTO
3. 3 Architecture Exercise— 4–6 hours
4. 4 Architecture Deep Dive— 90 min collaborative review
5. 5 Values & Fit— 45 min conversation
6. 6 References & Offer

Total timeline: 2–3 weeks