Staff Software Engineer, Data Platform

Invoca is an AI-powered revenue execution platform that connects marketing, commerce, and contact center teams to turn customer interactions into measurable growth. The Staff Software Engineer will provide technical leadership for the Data Platform team, define its technical direction, and own a rolling 3-month delivery plan. The role focuses on building reliable data infrastructure, enabling AI agent context, standardizing platform practices, and connecting technical outcomes to business impact.

Responsibilities:

• 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 platform's primary internal customers — to prioritize the 3-month plan based on measurable business and customer impact, not technical elegance alone

Requirements:

• 8+ years of software engineering experience with deep expertise in data platform, infrastructure, or backend systems, and a track record of measurable impact in production environments
• Proven technical leadership background: establishing team vision and architecture, managing an iterative delivery roadmap, and taking accountability for successful releases
• Experience building data systems, tools, or practices that were adopted broadly because they demonstrably improved speed, quality, reliability, or safety
• Experience building AI tools and agentic workflows, and a track record of practically applying new tools rather than just being interested in them
• Strong technical judgment with a bias toward simplicity and adoption over theoretical completeness
• Ability to connect technical work to business and customer outcomes, and to communicate that connection clearly
• Experience partnering across engineering, product, and business functions
• High ownership mindset, curiosity, and urgency in a fast-moving, evolving technical environment
• Candidates must be based within ~2 hour drive of these areas
• Occasional business travel may be required
• Hands-on experience with Databricks or a comparable lakehouse platform, plus modern pipeline orchestration tooling, strongly preferred
• Experience serving data to AI systems as context — retrieval, embeddings, feature or context serving, or similar — is a strong plus