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 Resume Review— 1–2 day turnaround

2 Technical Screen— 60 min video conversation with CTO

3 Architecture Exercise— 4–6 hours

4 Architecture Deep Dive— 90 min collaborative review

5 Values & Fit— 45 min conversation

6 References & Offer

Total timeline: 2–3 weeks

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