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Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation

Impress the toughest customers around – students – by building the analytical models which drive the design cycle for fun and compelling apps, helping them understand their students by building modular data models

Hunt and prospect into enterprise accounts and generate meetings with stakeholders across all levels, from C-suite executives to data engineers.

Architect and own CodeRabbit's BigQuery warehouse as the canonical analytical layer, building on the existing GCP and Fivetran foundation and taking it to a governed, investor-grade standard.

Design, build, and manage reliable data ingestion and transformation pipelines using Airflow, dbt, Python, Matillion, and Snowflake.

Assemble large, complex data sets that meet business requirements through extraction, transformation, and loading of data from a wide variety of data sources.

Contractor: Between €260/day and €350/day

AI-Augmented Intelligence: In close partnership with functional leaders, architect and deploy agent-assisted intelligence systems that move beyond static dashboards. Build workflows that proactively reason across silos (e.g.

Own the CRM enrichment strategy — design and maintain the data foundation our GTM org runs on, spanning account and contact data quality, enrichment workflows, and data provider orchestration across our stack (Clay, Harmonic

Semantic Layer Architecture: Build and maintain high-fidelity dbt and SQL models that serve as the "ground truth" for our complex, usage-based revenue models.

Design for Action: Build AI integrations that do more than just summarize text. Design systems that can securely read from and write back to our core platforms (e.g.