Environmental Data Analyst

Responsibilities

• Support environmental investigation projects by maintaining relational databases of environmental chemistry data from historical reports, laboratory test results, web sources, and third-party databases.
• Build and maintain ETL (extract, transform, load) and analysis workflows in SQL, Python, and/or R to clean, transform, validate, and analyze environmental datasets.
• Work with multi-disciplinary teams of engineers and scientists to support statistical and spatial analyses.
• Build dashboards and visualizations, and format tables, charts, and graphs to style guidelines for technical reports and presentations.
• Organize data in structures that support automation and downstream analysis.
• Perform quality assurance on analytical databases to ensure data integrity, and identify and correct data gaps, errors, and relational table issues.
• Support a wide variety of projects ranging from litigation support and site investigation to data mining for research and development.
• Effectively communicate findings to internal and external clients.
• Complete assigned tasks on schedule and within budget while meeting quality standards on deliverables.

Qualifications

• BS/BA or higher degree in data science, computer science, statistics, environmental science, or a related field.
• 2-5 years of work experience in data management or analysis, with strong relational database skills using PostgreSQL, Microsoft SQL Server, Microsoft Access, or other database management systems.
• Comfortable writing advanced SQL for data management (beyond basic create, read, update, delete), to clean, transform, and structure data in relational databases.
• Familiarity with relational database design, data modeling, and normalization.
• Awareness of data compilation, migration, and backup/retention considerations.
• Shell scripting (Bash) and comfort working in Linux and command-line environments.
• Experience using Python and/or R (Tidyverse, Shiny) for data analysis, visualization, dashboards, and web applications, including hands-on project work.
• Self-motivated and eager to learn, with a drive to adopt new data and analysis techniques, AI, and emerging technologies to improve workflows.
• Excellent written and verbal communication, and the ability to manage multiple tasks and meet deadlines with limited supervision.
• Strong problem-solving skills and attention to detail.

Preferred Skillsets and Qualifications

• Familiarity with chemistry data, laboratory information management systems (LIMS), data validation, and extraction of data from online sources.
• Version control (Git) and reproducible, scripted analysis workflows.
• Application development and deployment, including web frameworks for data tools (e.g., Django, Flask/FastAPI, Dash, Streamlit, R Shiny).
• Familiarity with containerization (Docker) is a plus.
• Experience with embedded and analytical database engines (e.g., SQLite, DuckDB) for local analysis, prototyping, and data interchange.