Core Engineer - Data Systems (Multiple Levels)

The Opportunity We’re looking for a Core Engineer focused on data systems to build the data and streaming foundations that make our edge platform reliable, observable, and repeatable. You’ll own core platform capabilities for ingesting, contextualizing, and serving customer data across OT/IT environments, and you’ll also build and support internal data systems that power Edgescale AI operations (observability, alerting, inventory, configuration, fleet state, and deployment telemetry). This role defines and enforces data contracts and integration standards, and can stop data flows that are not ready for production. You’ll ensure data is accurate and traceable, so data used by AI and operations can be trusted. You’ll partner with software, infrastructure, security, and commercial teams to translate requirements and feedback into durable platform capabilities that scale. This work is AI-native: you’ll use AI to speed up development and troubleshooting while keeping all production data paths reviewed and auditable. This is a hands‑on role for someone who thrives in a high‑ownership setting and wants to build the infrastructure that makes real‑world AI possible. What You’ll Do Own core capabilities for ingesting, contextualizing, and serving data across edge and hybrid environments. Build and evolve connectors and integration patterns across OT/IoT protocols and enterprise systems, with strong reliability and observability. Implement and operate real‑time streaming and event‑driven data flows for low‑latency use cases with clear failure modes. Define and enforce data contracts, schemas, and integration standards so producers and consumers operate predictably, and stop data flows that are not production‑ready. Ensure data accuracy and traceability end‑to‑end, including lineage, auditability, and validation mechanisms so AI and operational systems can trust the data. Build and support internal data systems for platform operations, including observability pipelines, alerting, inventory/configuration databases, and fleet telemetry. Design scalable query and access patterns that support analytics and AI, including federated query and time‑series access patterns. Partner with customer‑facing teams for requirements and feedback, then translate learnings into durable platform capabilities that scale. Maintain crisp engineering documentation and reusable artifacts, using AI tools to accelerate implementation, debugging, and iteration loops, then refining with engineering judgment and rigorous review for production paths. What Success Looks Like In your first 3 months, you will have: Shipped a critical data system (customer‑facing or internal) that delivers measurable improvements in reliability, latency, observability, or integration speed. Established clear data contracts and validation mechanisms for at least one production‑critical data flow, improving trust, traceability, and operational confidence. Earned trust through autonomy and execution—becoming the go‑to owner for a meaningful slice of the data platform and internal telemetry systems. In your first year, you will be: Owning major components of the data platform and internal data systems end‑to‑end with clear accountability for production outcomes and platform reliability. Driving improvements that materially compress adoption cycles by making integrations faster, safer, and more repeatable across deployments. Shaping platform direction through scalable patterns across connectors, streaming, semantics, governance, and observability that unlock new AI and operational use cases. Who You Are 6+ years building and operating production data systems, streaming systems, or integration platforms across complex environments. Experience operating real‑time pipelines with strong reliability practices (e.g., Kafka‑style event systems, robust observability/alerting, on‑call readiness, incident response, and clear failure modes). Strong engineering craft: clean implementations, thoughtful designs, operational clarity, and strong documentation (e.g., Python and/or Go, APIs for ingestion/access, and structured testing). Comfort working in ambiguity and making sound trade‑offs under real constraints (latency, bandwidth, security, deployment timelines, and operational risk). Clear communicator and strong collaborator across engineering and customer‑facing teams, with the ability to define standards and enforce production discipline. Ownership mindset: outcomes over tasks. Unique Experiences We Value Production experience with streaming and event‑driven systems (e.g., Kafka) and operating them reliably under real‑world constraints. Experience with federated query systems and large‑scale analytics access patterns (e.g., Trino) and designing access patterns that support both operational use and AI workflows. Building data integration for OT/IoT environments, including protocols such as MQTT, OPC UA, Modbus, and DNP3, and handling schema drift, data quality, and connectivity variability. Experience building trusted data systems with contracts, validation, and lineage (e.g., schema enforcement, traceability, auditability, and mechanisms to block unsafe production data paths). Partnering with customer‑facing teams to turn messy integrations and requirements into repeatable platform capabilities, while keeping production systems reviewed, auditable, and operationally sound. Benefits We work in a high‑ownership, real‑world startup environment where you’ll move fast, build new systems, and see your impact immediately—what you ship runs in the field and drives measurable customer outcomes. We work alongside AI every day. Writing static code, docs, or plans “by hand” is no longer accepted—here you’ll use the latest AI tools to iterate and ship faster and to apply AI with our customers at scale. You’ll take on elite technical challenges at the frontier of infrastructure, including next‑generation cloud and IoT, hardware/software/networking in real‑world edge environments, the foundation for data and AI inference, and industry‑leading secure systems in demanding operational (OT) settings. You’ll learn fast by working with exceptional teammates and collaborating directly with industry leaders as partners in software, AI, and infrastructure. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. This role may be filled at the Senior or Staff level, with a base salary range of $130,000–$155,000 (Senior) or $160,000–$185,000 (Staff). Total compensation for this role includes equity in your work. You are eligible for meaningful equity through stock options in an early‑stage, high‑growth company. You are eligible to participate in company benefit plans, which may include health, dental, and vision coverage, a 401(k) with company match, flexible PTO, paid parental leave, commuter benefits, and relocation and visa support for eligible roles. Edgescale AI At Edgescale AI, we’re deploying AI in the real world—helping customers apply this technology to unlock transformative productivity gains. Our work sits at the intersection of infrastructure, security, networking, and AI, where reliability and performance are non‑negotiable and where solutions demand deep, distributed systems thinking. We’re intensely AI‑native. We build with AI, we ship AI, and we use it every day to accelerate how we design, test, deploy, and operate complex systems. If you want to help pave the application of AI in the real world, at global scale, we want to hear from you. Edgescale AI is building an inclusive, merit‑based organization. We are an equal opportunity employer and do not discriminate on any legally protected status. We value diversity, inclusion, and a shared passion for creating real‑world impact. #J-18808-Ljbffr Alumni Ventures