Analytics Engineer I

ECC•Burlingame, CA

4h•$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: Data Engineering: Design, develop, and maintain Azure-based ELT pipelines to ingest, clean, transform, and publish data. Ensure robust data quality, monitoring, and governance standards. Data Optimization: Improve efficiency and scalability of SQL queries, stored procedures, and Spark/PySpark/Databricks jobs. Automation & Integration: Create Python automation scripts for API ingestion, web scraping, and scheduled data updates. Integrate external data sources securely into Azure Data Lake and Azure SQL. Power BI Modeling: Build and publish enterprise datasets using star schema design, row-level security, incremental refresh, and deployment pipelines. Partner with BI analysts to enhance refresh times and model reliability. AI Initiatives: Develop and deploy machine learning models for business insights, leveraging modern AI frameworks and tools. Collaborate on research into advanced analytics and predictive modeling pipelines, aligning solutions with industry best practices and integrating them with data lake and data warehouse environments. Cross-Team Collaboration: Engage with HR, Operations, Scheduling, and Finance teams to define data requirements and participate in code reviews, contributing to shared repository standards. Be a Self-Starter: Proactively identify opportunities and initiate actions that drive progress, without waiting for direction. Think Broadly: Apply strategic and holistic thinking to project work, keeping in mind how individual actions contribute to the organization’s long-term goals and success. Documentation & Support: Maintain thorough documentation, runbooks, monitor dashboards, and architecture diagrams. Provide support for production pipelines during deployments and major program 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 (ADF/Synapse, ADLS, Azure SQL, Key Vault, Functions, Fabric, Purview).
Proficient in Python for data analysis, automation, APIs, and scraping frameworks.
Power BI dataset modeling experience (DAX fundamentals, RLS, incremental refresh).
Experience with Git and CI/CD pipelines.
Understanding of data quality, observability, and structured documentation.

Responsibilities

Design, develop, and maintain Azure-based ELT pipelines to ingest, clean, transform, and publish data.
Ensure robust data quality, monitoring, and governance standards.
Improve efficiency and scalability of SQL queries, stored procedures, and Spark/PySpark/Databricks jobs.
Create Python automation scripts for API ingestion, web scraping, and scheduled data updates.
Integrate external data sources securely into Azure Data Lake and Azure SQL.
Build and publish enterprise datasets using star schema design, row-level security, incremental refresh, and deployment pipelines.
Partner with BI analysts to enhance refresh times and model reliability.
Develop and deploy machine learning models for business insights, leveraging modern AI frameworks and tools.
Collaborate on research into advanced analytics and predictive modeling pipelines, aligning solutions with industry best practices and integrating them with data lake and data warehouse environments.
Engage with HR, Operations, Scheduling, and Finance teams to define data requirements and participate in code reviews, contributing to shared repository standards.
Proactively identify opportunities and initiate actions that drive progress, without waiting for direction.
Apply strategic and holistic thinking to project work, keeping in mind how individual actions contribute to the organization’s long-term goals and success.
Maintain thorough documentation, runbooks, monitor dashboards, and architecture diagrams.
Provide support for production pipelines during deployments and major program cycles.