Core Engineer - Data Systems (Multiple Levels)

Edgescale AI•Denver, CO

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.