Senior/Staff Software Engineer (Data Platform)

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.

Actively's agents make decisions in real time, which accounts to prioritize, what actions to take, when to involve a human. All of that reasoning runs on data: CRM records, call transcripts, external signals, and customer-specific context pulled from dozens of sources. When that data is stale, malformed, or missing context, the agents get it wrong.

You'll build and scale the data foundation that every agent, insight, and workflow at Actively depends on; designing pipelines that handle diverse, often messy inputs and turn them into clean, structured, agent-ready representations. At scale, that means millions of accounts, each with their own data shapes, business rules, and edge cases, all needing to stay fresh and reliable.

The challenge isn't just throughput. It's building infrastructure that's opinionated enough to enforce quality and consistency, but flexible enough to adapt as new data sources, customer configurations, and agent capabilities keep evolving.

• Own the ingestion and transformation layer. Design and scale pipelines that pull structured and unstructured data from CRM systems, call transcripts, and external signals, normalizing and enriching it into representations agents can reason over in real time.
• Build for operational use, not just analytics. The data you produce doesn't power dashboards; it powers decisions. Freshness, accuracy, and low-latency access matter here in ways they don't in a typical data warehouse.
• Keep data current as the world changes. Architect real-time and mini-batch workflows using technologies like Pub/Sub, Kafka, or modern ETL tools to ensure data stays synchronized as customer activity happens.
• Solve for customer-specific variation at scale. Every customer has their own CRM configuration, field naming, and business logic. You'll build transformation systems that stay consistent and correct across all of them without becoming brittle.
• Own reliability end to end. Observability, lineage, schema management, alerting; you define what "trust in the data" means and make sure it holds across thousands of accounts, so agents and other teams can confidently build on top of it.
• Work across the full stack. Python, SQL, DBT, BigQuery, Snowflake and move between layers fluidly, contributing wherever the work needs it.

Who You Are

• Deep roots in data systems, not just data tooling. You have 5+ years designing and operating core data infrastructure from ingestion and transformation to serving and observability in high-growth environments where the data needed to be right, fresh, and fast.
• Built for agents and models, not just reports. You've worked on data systems that power ML models, intelligent workflows, or real-time decisioning. You understand the different demands that put on infrastructure compared to a typical analytics stack.
• Fluent across the modern data stack. Proficient in Python, SQL, and DBT, with hands-on experience in BigQuery or Snowflake, and familiar with orchestration tools like Fivetran, Airflow, or Polytomic.
• Fluent in real-time infrastructure. You've built streaming and mini-batch pipelines using Pub/Sub, Kafka, Dataflow, or similar technologies, and understand the trade-offs between latency, throughput, and operational complexity.
• Startup-proven or product-platform experience. You've either built a data platform from scratch at an early-stage company or worked at a data-focused product company (e.g. Segment, dbt Labs) scaling systems across many customers.
• Self-directed and accountable for quality. You take work from design to production without being managed through it, and you hold yourself responsible for whether the data your systems produce is actually trustworthy.

• Prior experience at a data infrastructure or platform company (e.g. Segment, Databricks, Confluent, Fivetran) or meaningful contributions to open-source data tooling.
• Familiarity with embedding and vector pipelines like chunking strategies, index management, and keeping representations in sync with fast-changing source data.
• Experience building data pipelines where correctness was a hard requirement like financial data, compliance systems, or other domains where bad data has real downstream consequences.