Software Engineer, Data Platform

Invoca is an AI-powered revenue execution platform that brings together marketing, commerce, and contact center teams to turn every customer interaction into measurable, profitable growth. Join our dynamic, fast-growing team, where innovation and collaboration are at the core of our culture.

At Invoca, AI is central to our engineering strategy. We are cultivating an agentic development ecosystem where artificial intelligence serves as an integral core partner in designing, building, testing, launching, and managing software.

The Data Platform team builds and innovates on Invoca's core data infrastructure and analysis tooling. Teams across the company depend on it to turn call and SMS conversations, and the marketing signals around them, into reliable, near real-time intelligence. We also supply the context the AI agents across Invoca's platform run on, and we work on the Data Intelligence products that put that data to work for customers.

We are seeking a Staff Software Engineer to provide technical leadership for the Data Platform team. In this role, you will help establish strategic technical direction and drive the team's rolling 3-month delivery plan, turning roadmap goals into executable, high-impact releases.

Success here is measured by outcomes, not activity: how reliable and timely our data is, how quickly downstream teams can build on the platform, and how consistently the team's plan turns into delivered work.

• Define and evolve the technical vision for Invoca's data platform across all product teams.
• Design and build the ingestion, streaming, and transformation pipelines that bring call and SMS conversation data and marketing signals into the lakehouse.
• Evolve the lakehouse architecture and the core data analysis tooling teams use to explore, query, and report on that data.
• Ensure product teams can safely and easily build on our data without deep data infrastructure expertise.
• Continuously identify where shared platform investments can unlock new product capabilities or eliminate duplicated engineering effort.

• Provide overarching architectural direction for the Data Platform across ingestion, transformation, and serving layers while raising the engineering bar through mentoring, design reviews, and hands-on guidance.
• Own and execute the rolling 3-month delivery plan: translate roadmap strategy into clear priorities, navigate ambiguity, and maintain full accountability for shipping high-impact technical outcomes.
• Drive alignment across engineering and product teams through clear technical reasoning, simplifying complex systems, and serving as a trusted advisor to senior leadership.

• Build the systems that let the AI agents running in Invoca's platform use our data as context - retrieval, freshness, structure, and governance of the data those agents depend on.
• Work on Invoca's Data Intelligence products, like Atlas, our data agent, turning platform capability into intelligence customers can act on.
• Define clean boundaries and contracts between the data platform and the agent and product teams that build on it.

• Use AI and agentic workflows in how the team builds and operates the platform: agent-assisted development, adaptive pipeline orchestration, automated data quality checks, anomaly detection, and self-healing jobs.
• Establish safe, observable, and auditable ways to bring AI into how we build and run data workflows, and help the teams around you adopt them with confidence.

• Build shared data platform practices, tooling, and self-service capability (pipeline frameworks, data contracts, lakehouse standards) that other teams adopt instead of one-off approaches.
• Treat the data platform as a product with internal users: define adoption goals and measure whether teams are actually better off because of your work.
• Drive direction and standards for data architecture and pipeline design across the teams that depend on the platform, influencing through clarity, judgment, and evidence.

• Translate data platform decisions into terms leadership and product teams care about: data freshness, reliability, cost, and the speed at which downstream teams can ship.
• Partner directly with product engineering, analytics, and data science teams - the…