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 RoleWe'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 DoDesign and implement the foundational data infrastructure from scratchSet up Snowflake with proper environments, security, and access controlsCreate architectural patterns that scale with the companyBuild robust pipelines from source systems to the data warehouseImplement transformations with dbt and orchestrate with Airflow/DagsterIntegrate Fivetran connectors and custom extraction from SupabaseData ModelingDesign dimensional models supporting both analytics and ML use casesCreate semantic layers that make data accessible to stakeholdersImplement slowly changing dimensions and proper data governanceBuild infrastructure feeding Sophie's machine learning capabilitiesCreate feature stores for real-time feature servingWhat We're Looking ForMust Have5+ years experience with modern cloud data warehouses (Snowflake strongly preferred)Extensive ETL/ELT pipeline development with strong SQL skillsdbt experience required; Airflow, Dagster, or Prefect for orchestrationStrong Python for data engineering tasksAWS experience (S3, Glue, Athena, Redshift)Great to HaveML pipeline experience (MLflow, Feast, feature stores)Fivetran or similar managed ELT toolsDimensional modeling expertise (Kimball methodology)Startup experience building data infrastructure from scratchBig data at scale (Spark, distributed computing)How You WorkArchitectural thinker who balances immediate needs with long-term maintainabilitySelf-directed and comfortable with high autonomyStrong communicator who can translate technical concepts for stakeholdersPragmatic about tradeoffs — knows when to build for scale vs. good enoughWhat This Role Is NotNot a Data Analyst role — you build infrastructure that enables analysisNot a Data Scientist role — you build ML pipelines; they build modelsNot a Backend Engineer role — you own the data layer, not the application layerBase Competitive — Based on experience and locationEquity Meaningful early-stage grant with 4-year vestingEquipment Professional laptop provided + remote work stipend after 6 monthsTime Off Flexible PTO with minimum 15 days encouragedSchedule Versátil hours with 3–4 hours daily overlap Americas timezonesInterview Process1Resume Review — 1–2 day turnaround2Technical Screen — 60 min video conversation with CTO3Architecture Exercise — 4–6 hours4Architecture Deep Dive — 90 min collaborative review5Values & Fit — 45 min conversation6References & OfferTotal timeline: 2–3 weeks#J-18808-Ljbffr

📌 Data Engineer (Senior) (Colombia)
🏢 Myprospera
📍 Colombia