Senior / Staff Software Engineer (Data Platform)

About Actively AI

Our thesis is that businesses of the future will be powered by agentic human-in-loop-machines that make every business function 10x more efficient.

Actively AI is building that superintelligent machine for Enterprise GTM organizations, focused on increasing productivity per rep. We power the day-to-day for outbound teams at dozens of companies like Samsara, Ramp, Verkada, and Ironclad.

Why does this matter? Because revenue is the ultimate fuel for businesses. The hundreds of millions of dollars we generate for our customers enables them to employ more people, innovate faster, and deliver more value to their customers.

In addition to top-notch customers that love our product, our team is incredibly high caliber - the co-founders are former Stanford AI researchers and the engineering team comes from Harvard, CMU, Berkeley, Brex, Scale AI, and Google. We're also backed by top investors, including Bain Capital Ventures, First Round Capital (seed investors in Uber, Square, Roblox, Clearbit), Lachy Groom, and Stanford AI faculty.

We have a very ambitious product and scaling roadmap, there’s strong market interest in what we are doing, and it’s time to put the foot on the gas. If you get excited by the thought of working really hard on these kinds of problems with a high caliber team, then Actively AI is the right place for you.

About the Role

We’re looking for a Senior/Staff Data Platform Engineer to build and scale the foundation of Actively’s data ecosystem — the pipelines, transformations, and infrastructure that power every agent, insight, and workflow across the company.

You’ll design systems that take in vast amounts of raw, often inconsistent data from many sources and turn it into structured, reliable information that Actively’s agents can reason over in real time. These systems will need to support diverse data shapes — from structured CRM tables to unstructured transcripts, documents, and external signals — and handle both high-volume throughput and nuanced customer-specific variations.

This role combines data architecture and infrastructure engineering at scale. You’ll build modular, schema-aware systems that are flexible enough to adapt to new data types and customer configurations, yet opinionated enough to stay consistent and maintain data quality. As the platform grows, you’ll ensure it remains performant, observable, and ready to serve increasingly complex workloads across thousands of accounts being operated by agents.

What You’ll Do

• Design and scale core data pipelines that process and transform high-volume structured and unstructured data into reliable, ready-to-use data models.
• Build data infrastructure for scale — ensuring performance, reliability, and flexibility as customer volumes and use cases expand.
• Develop modular transformation frameworks that handle diverse data types and customer-specific schemas while maintaining consistency and quality.
• Architect real-time and mini-batch workflows using technologies like Pub/Sub, Kafka, or modern ETL tools to keep data fresh and synchronized.
• Work across the data stack — from ingestion and storage to orchestration and serving — using tools such as Python, SQL, DBT, and BigQuery/Snowflake.
• Drive data excellence — define best practices around observability, lineage, schema management, and governance to maintain trust in every dataset.

Who You Are

• A data platform builder. You have 5+ years of experience designing and scaling core data systems — from ingestion and transformation to serving and observability — in high-growth or product-focused environments.
• Startup-proven or product-platform experience. You’ve either built a data platform from the ground up at an early-stage company or worked at a data-focused product company (e.g. Segment, dbt Labs) scaling systems across many customers.
• Experience across the modern data stack. Proficient in Python, SQL, and DBT, with hands-on experience in BigQuery, Snowflake or equivalent systems, and familiar with ETL and orchestration tools like Fivetran, Airflow, or Polytomic.
• Fluent in real-time data systems. Comfortable building streaming or mini-batch pipelines with Pub/Sub, Kafka, Dataflow or similar event-driven technologies.
• Applied intelligence mindset. You’ve worked on data systems powering ML models, intelligent workflows, or real-time decisioning, not just analytics or reporting.
• Strong Ownership. You take ownership from design to deplo