Environmental Data Analyst

GSI Environmental Inc.•Olympia, WA

2d•$76,000 - $110,000•Hybrid

About The Position

GSI is seeking an Environmental Data Analyst who combines technical ability, analytical creativity, and business acumen to strengthen our data management, analysis, and visualization team. The ideal candidate is comfortable across the full data lifecycle—building and maintaining relational databases, developing analysis and automation pipelines in SQL, Python, and/or R, and providing query, reporting, and visualization support in a collaborative organization working closely with technical staff. Familiarity with environmental chemistry data generated from field investigations of water, soil, sediment, air, and biological matrices is valued. The candidate should know how to analyze system requirements and be familiar with relational databases, statistical analysis, and modern data visualization techniques using relevant and current tools. GSI provides high-quality technical and regulatory expertise to industry and government to address a broad range of environmental issues. We specialize in solving complex environmental problems by leveraging our extensive knowledge of environmental regulations, stressing accurate data collection and analysis, and applying new technologies to our clients' most difficult challenges.

Requirements

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.

Nice To Haves

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.

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.