Role Summary

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.

Preferred Qualifications (Nice-to-Haves)

- Exposure to consumer finance or retail domain metrics (e.g., P&L; statements, cohort analysis, retention metrics).

- Familiarity with multi-tenant BigQuery consumption architectures and Model Context Protocol (MCP) servers.

- Prior experience in high-growth, early-to-mid-stage SaaS startups

Competencies / Soft Skills

- Ownership &
- Autonomy:

Takes complete responsibility for data pipeline health, proactively fixing issues independently without waiting for step-by-step approval.

- Pragmatism &

- Speed:

Prioritizes fast, effective issue resolution to keep customer operations running smoothly over theoretical code perfection.

- Proactive Communication:

Keeps cross-functional stakeholders updated with high clarity and frequency during open production incidents.

- Adaptability:

Easily shifts focus when production priorities emerge in a fast-paced startup environment.

📌 Senior Data Platform Engineer (Argentina)
🏢 Waverley Software
📍 Argentina