The Role Build and operate production data and streaming systems across edge, hybrid, OT, IoT, and enterprise environments. Responsibilities include developing connectors, real-time event-driven pipelines, data contracts, schemas, validation, lineage, observability, telemetry, federated query patterns, and reliable integration platforms supporting analytics and AI. The role requires strong ownership, system design, documentation, incident readiness, and collaboration with engineering and customer-facing teams. Summary Generated by Built In Core Engineer — Data Systems (Multiple Levels) Hybrid — Denver, CO Full-time The Opportunity We're looking for a Core Engineer focused on data systems to build the data and streaming foundations that make a real-world technology platform reliable, observable, and repeatable. You'll own core platform capabilities for ingesting, contextualizing, and serving customer data across OT/IT environments. You'll also build and support internal data systems that power platform operations, including observability, alerting, inventory, configuration, fleet state, and deployment telemetry. This role defines and enforces data contracts and integration standards, ensuring that data flows are production-ready. You'll help ensure data is accurate, traceable, and trustworthy for both AI-driven and operational systems. You'll partner with software, infrastructure, security, and commercial teams to translate requirements and feedback into durable platform capabilities that scale. This is an AI-native environment where AI tools are used to accelerate development and troubleshooting while maintaining rigorous review and auditability for production data paths. This is a hands-on opportunity for someone who thrives in a high-ownership setting and wants to build the infrastructure that supports real-world AI applications. 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 clearly defined failure modes. Define and enforce data contracts, schemas, and integration standards so producers and consumers operate predictably. Ensure data accuracy and traceability end-to-end, including lineage, auditability, and validation mechanisms. Build and support internal data systems for platform operations, including observability pipelines, alerting, inventory and 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 to understand requirements and feedback, then translate those learnings into durable platform capabilities. Maintain clear engineering documentation and reusable artifacts, using AI tools to accelerate implementation, debugging, and iteration while applying sound engineering judgment and rigorous production review. What Success Looks Like In your first 3 months, you will have: Shipped a critical data system, either 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 portion of the data platform or 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 reliability. Driving improvements that make integrations faster, safer, and more repeatable across deployments. Shaping platform direction through scalable patterns across connectors, streaming, semantics, governance, and observability that enable new AI and operational use cases. Who You Are 6+ years of experience building and operating production data systems, streaming systems, or integration platforms across complex environments. Experience operating real-time pipelines with strong reliability practices, such as Kafka-style event systems, robust observability and alerting, on-call readiness, incident response, and clearly defined failure modes. Strong engineering craft, including clean implementations, thoughtful system design, operational clarity, and strong documentation. Experience with technologies such as Python and/or Go, ingestion and access APIs, and structured testing. Comfortable working in ambiguous environments and making sound trade-offs under real-world constraints involving latency, bandwidth, security, deployment timelines, and operational risk. Clear communicator and strong collaborator across engineering and customer-facing teams. Ability to define standards and enforce production discipline. Ownership mindset focused on outcomes rather than tasks. Unique Experiences We Value Production experience with streaming and event-driven systems, such as Kafka, and operating them reliably under real-world constraints. Experience with federated query systems and large-scale analytics access patterns, such as Trino, supporting both operational and AI workflows. Experience building data integrations for OT/IoT environments, including protocols such as

MQTT, OPC

UA, Modbus, and DNP3. Experience handling schema drift, data quality, and connectivity variability. Experience building trusted data systems with contracts, validation, and lineage, including schema enforcement, traceability, auditability, and mechanisms to prevent unsafe production data paths. Experience partnering with customer-facing teams to turn complex integrations and requirements into repeatable platform capabilities. Strong focus on keeping production systems reviewed, auditable, and operationally sound. Benefits & Compensation Work in a high-ownership, real-world startup environment where you can move quickly, build new systems, and see your impact directly. Use modern AI tools throughout development, documentation, troubleshooting, and deployment workflows to accelerate iteration and execution. Take on challenging technical problems at the intersection of infrastructure, cloud, IoT, hardware/software systems, networking, data, and AI. Collaborate with exceptional teammates and industry leaders across software, AI, and infrastructure. This role may be filled at either the Senior or Staff level .

Base salary range:

Senior:

$130,000-$155,000

Staff:

$160,000-$185,000 Eligibility for meaningful equity through stock options in an early-stage, high-growth company. Eligibility 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. Read Full Description Skills Required 6+ years of experience building and operating production data systems, streaming systems, or integration platforms Experience operating real-time pipelines with reliability practices, observability, alerting, on-call readiness, incident response, and defined failure modes Strong engineering craft, including clean implementations, system design, operational clarity, and documentation Experience with Python and/or Go, ingestion and access APIs, and structured testing Ability to make sound trade-offs involving latency, bandwidth, security, deployment timelines, and operational risk Clear communication and collaboration across engineering and customer-facing teams Ability to define standards and enforce production discipline Ownership mindset focused on outcomes rather than tasks Production experience with streaming and event-driven systems such as Kafka Experience with federated query systems such as Trino Experience building OT/IoT data integrations using

MQTT, OPC

UA, Modbus, or DNP3 Experience handling schema drift, data quality, and connectivity variability Experience building trusted data systems with contracts, validation, lineage, traceability, and auditability Experience translating complex customer integrations into repeatable platform capabilities SourceDirect Talent Compensation & Benefits Highlights The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about SourceDirect Talent and has not been reviewed or approved by SourceDirect Talent. About This Summary Affordable Benefits — Some postings describe employer-covered health premiums for employees and substantial support for dependents, signaling low out-of-pocket costs where eligible. Offerings may vary by role or employer-of-record, but the described coverage indicates strong affordability in certain cases. Retirement Support — Listed packages for select roles include a 401(k) with company matching, with one description characterizing the plan as among the strongest of its kind. These elements point to meaningful long-term savings support when applicable. Flexible Benefits — Descriptions highlight remote work options, flexible schedules, and home-office or coworking stipends, alongside learning support and occasional company off-sites. Such features suggest benefits that adapt to different working arrangements. Learn more about SourceDirect Talent's Compensation & Benefits → SourceDirect Talent Insights What's It Like to Work at SourceDirect Talent? SourceDirect Talent Culture & Values SourceDirect Talent Career Growth & Development What's the Work-Life Balance Like at SourceDirect Talent? SourceDirect Talent Leadership & Management SourceDirect Talent Company Growth, Stability & Outlook View all jobs at SourceDirect Talent View SourceDirect Talent Profile Report Job