Analytics Engineer I

ECC•Lakewood, CO

12d•$75,762 - $103,840•Remote

About The Position

ECC provides essential environmental, construction, and disaster response services, relying on its Data Science & Analytics Team for enterprise data pipelines, automation, Power BI datasets, and AI initiatives. As an Analytics Engineer, you transform data and technology into actionable insights to drive growth and improve performance. You design and maintain data infrastructure, develop predictive and AI-driven solutions, and collaborate with business units to optimize personnel selection, safety and core KPIs. In addition to technical expertise, you embody ECC’s vision and values—building trust, fostering collaboration, and encouraging innovation to support both team and stakeholder success. In this position, you will: AI & Intelligent Systems Engineering: Design and deploy enterprise AI solutions using LLMs, RAG architecture, and intelligent agents aligned with ECC business needs. Copilot & Agent Development: Build and maintain copilots and AI agents using Microsoft Copilot Studio and Microsoft Agents SDK, integrating workflows, guardrails, and telemetry. Computer Vision & Detection Models: Develop, train, and deploy computer vision models for object detection, classification, and image analysis to support operational, safety, and analytical use cases. Data Foundations for AI: Engineer and manage Azure‑based data pipelines that power AI retrieval, embeddings, and analytics workloads. Automation & AI Integration: Develop Python‑based automation and integrations for APIs, document ingestion, embeddings, and AI pipeline orchestration. Analytics & Decision Enablement: Enable AI‑driven insights by supporting trusted semantic models and datasets used across reporting, copilots, and executive workflows. Cross‑Functional Collaboration: Partner with business and technical teams to define AI use cases, data requirements, and success metrics. Governance, Quality & Observability: Ensure AI and data solutions meet standards for security, reliability, documentation, transparency, and responsible use. Documentation & Production Support: Maintain clear technical documentation and support production AI systems during deployments and critical business cycles.

Requirements

Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, or a related field with at least 2 years of professional experience in data engineering, BI engineering, or analytics engineering; or a master’s degree in one of these fields with at least 1 year of professional experience.
Strong SQL includes window functions, performance tuning, and relational modeling.
Hands‑on experience with Azure (AI Search, ADF/Synapse, ADLS, Azure SQL, Key Vault, Functions, Fabric, Purview).
Hands-on experience with Computer vision models for classification, detection and segmentation using cloud services or offline models
Proven experience with LLMs, RAG architecture, embeddings, prompt engineering, and evaluation techniques in an enterprise setting.
Proficiency in Python for data engineering, AI pipelines, APIs, document processing, and automation.
Experience building copilots or intelligent agents using Copilot Studio, Microsoft Agents SDK, or comparable AI orchestration frameworks.
Experience with Git and CI/CD pipelines.
Understanding of data quality, observability, and structured documentation.

Responsibilities

Design and deploy enterprise AI solutions using LLMs, RAG architecture, and intelligent agents aligned with ECC business needs.
Build and maintain copilots and AI agents using Microsoft Copilot Studio and Microsoft Agents SDK, integrating workflows, guardrails, and telemetry.
Develop, train, and deploy computer vision models for object detection, classification, and image analysis to support operational, safety, and analytical use cases.
Engineer and manage Azure‑based data pipelines that power AI retrieval, embeddings, and analytics workloads.
Develop Python‑based automation and integrations for APIs, document ingestion, embeddings, and AI pipeline orchestration.
Enable AI‑driven insights by supporting trusted semantic models and datasets used across reporting, copilots, and executive workflows.
Partner with business and technical teams to define AI use cases, data requirements, and success metrics.
Ensure AI and data solutions meet standards for security, reliability, documentation, transparency, and responsible use.
Maintain clear technical documentation and support production AI systems during deployments and critical business cycles.