Senior Data Platform Engineer

As a Senior Data Platform Engineer, you will own and evolve the foundational infrastructure that powers our entire suite of AI and financial analytics products. Working with high autonomy in a remote environment aligned with European/US EST overlap hours, you will ensure high platform availability, rapid incident resolution, robust data validation, and optimal pipeline scalability across our multi-tenant architecture.

Key Responsibilities

• Enhance Platform Reliability: Drive fault tolerance, observability, and data quality across the full data stack (Source → Airbyte → dbt → BigQuery → MCP Server / Applications).
• Rapid Incident Response: Investigate and resolve production data bugs with a target ~30-minute turnaround time, maintaining stakeholder communication updates every 45 minutes during open incidents.
• Pipeline Validation & Testing: Partner with Product Managers (who build initial pipeline feature changes) to validate dbt models and test downstream dependencies, preventing data breakages.
• Expand Data Integrations: Build and maintain scalable connectors from external sources (e.g., Airbyte custom YAML configs, Shopify, Amazon, QuickBooks, Snowflake, Redshift).
• Performance Optimization: Optimize queries and dbt/Airbyte pipelines for maximum speed, low latency, and cost efficiency.
• Next-Gen Architecture: Architect and refine our data platform to support multi-tenant BigQuery consumption, large-scale growth, and MCP server data integration.
• Leverage AI Workflows: Utilize agentic AI coding tools (e.g., Claude Code) daily to accelerate investigations, testing, and production fixes.

Required Qualifications

• 6+ years of experience in Data Engineering or backend data platform roles.
• Core Technical Skills: Advanced expertise in SQL-based data warehousing, specifically BigQuery.
• Pipeline Tools: Proven hands-on experience building, scaling, and optimizing ELT/ETL pipelines using dbt and Airbyte (including custom YAML file configurations).
• Software Engineering & Validation: 4+ years of experience with Python and SQL focused on data testing, validation, and pipeline health.
• AI Tooling Mandate: Active, daily experience using Claude Code or comparable agentic AI coding tools to debug and ship code rapidly in production.
• Working Hours Flexibility: Ability to cover the US EST business window.
• Experience with system integrations and orchestration.
• Strong communication skills, with the ability to articulate complex data science concepts and architectural trade-offs to client-side technical leadership (CTOs, Lead Data Scientists).