Staff Software Engineer, Data Platform

Invoca•San Francisco, CA

2d•Remote

About The Position

Invoca is seeking a Staff Software Engineer to provide technical leadership for its Data Platform team. The team builds the core data infrastructure and analysis tools that turn call and SMS conversations and related marketing signals into reliable, near-real-time intelligence. Its platform also supplies context for AI agents and supports customer-facing Data Intelligence products such as Atlas. The role sets technical direction across the data platform and owns a rolling three-month delivery plan, translating roadmap goals into priorities and high-impact releases. The engineer will build and evolve ingestion, streaming, transformation, and serving systems; improve lakehouse architecture and analysis tooling; and enable product teams to use data safely without requiring deep infrastructure expertise. The Staff Engineer will guide architecture across teams through hands-on work, mentoring, design reviews, and clear technical reasoning. They will partner with engineering, product, analytics, and data science teams to prioritize work based on measurable business and customer impact. The role also advances safe and observable uses of AI and agentic workflows in platform development and operation, and establishes shared practices and self-service capabilities that are broadly adopted. This is a remote-first role for candidates based within approximately a two-hour drive of the listed U.S. locations: Greater Los Angeles Area (including Santa Barbara and San Diego), San Francisco Bay Area, Denver Metro, Austin Metro, Chicago Metro, Greater New York City Area, or Seattle Metro. Occasional business travel may be required.

Requirements

At least 8 years of software engineering experience with deep expertise in data platforms, infrastructure, or backend systems.
A track record of measurable impact in production environments.
Proven technical leadership, including establishing team vision and architecture, managing an iterative delivery roadmap, and taking accountability for successful releases.
Strong technical judgment and a bias toward simplicity and adoption over theoretical completeness.
Ability to connect technical work to business and customer outcomes and communicate that connection clearly.
Experience partnering across engineering, product, and business functions.
High ownership, curiosity, and urgency in a fast-moving, evolving technical environment.

Nice To Haves

Hands-on experience with Databricks or a comparable lakehouse platform and modern pipeline orchestration tooling.
Experience serving data to AI systems as context, such as through retrieval, embeddings, feature serving, or context serving.
Experience building data systems, tools, or practices that achieved broad adoption by demonstrably improving speed, quality, reliability, or safety.
Experience building AI tools and agentic workflows, with practical application of new tools.

Responsibilities

Define and evolve the technical vision for Invoca's data platform across product teams.
Design and build ingestion, streaming, and transformation pipelines for call and SMS conversation data and marketing signals.
Evolve the lakehouse architecture and core data analysis tooling used to explore, query, and report on data.
Enable product teams to build safely and easily on the data platform without deep data infrastructure expertise.
Identify shared platform investments that enable product capabilities or eliminate duplicated engineering effort.
Provide architectural direction across ingestion, transformation, and serving layers; mentor engineers and provide design reviews and hands-on guidance.
Own and execute the rolling three-month delivery plan, translating roadmap strategy into priorities and delivering high-impact technical outcomes.
Align engineering and product teams through clear technical reasoning and advise senior leadership.
Build systems that provide AI agents with data context, including retrieval, freshness, structure, and governance.
Contribute to Data Intelligence products such as Atlas and define clear boundaries and contracts between the platform and agent and product teams.
Apply AI and agentic workflows to platform development and operations, including agent-assisted development, adaptive pipeline orchestration, automated data quality checks, anomaly detection, and self-healing jobs.
Establish safe, observable, and auditable practices for AI in data workflows and support adoption by other teams.
Build shared data platform practices, tooling, and self-service capabilities, including pipeline frameworks, data contracts, and lakehouse standards.
Treat the platform as a product for internal users by defining adoption goals and measuring outcomes.
Set direction and standards for data architecture and pipeline design across dependent teams.
Communicate platform decisions in terms of data freshness, reliability, cost, and downstream delivery speed.
Partner with product engineering, analytics, and data science teams to prioritize work by measurable business and customer impact.

Benefits

U.S. health benefits begin on the first day of employment.
Medical, dental, and vision coverage.
Mental wellbeing support and an Employee Assistance Program.
Wellness subsidy that can be used toward gym memberships, fitness classes, and more.
Flexible paid time off.
20 U.S. paid holidays, including a winter break.
Up to 12 weeks of 100% paid family leave for baby bonding, adoption, and caring for family members.
Up to 12 weeks of 100% paid medical leave for childbirth and medical needs.
Access to enterprise-grade AI tools, including leading LLMs and agentic AI workflow solutions.
Up to $1,000 per fiscal year for professional development, learning, certifications, and conferences.
401(k) plan through Fidelity with company match of up to 4%.
Company-wide and team recognition programs, including President's Club and quarterly and departmental awards.
Sabbatical after seven years of service.
Stock option program for employees.
Bonus and/or commission eligibility, where applicable.