FNI is seeking a hands-on Data Engineer & Analytics Developer to transform complex, inconsistent, and disparate data into trusted, analysis-ready information, meaningful insights, and working analytical tools. The primary emphasis of this role is data engineering, accessing and assessing source data, building repeatable extract-transform-load (ETL/ELT) processes, improving data quality, and producing reliable data models and outputs. The role also conducts analysis and builds interactive dashboards, reports, and geospatial or other data-driven tools. The ideal candidate is detail-oriented and comfortable with in depth data exploration, bringing strong data management practices to their work to ensure data, code, transformations, metadata, and analytical outputs are organized, documented, traceable, and maintainable.
This position partners with a business analyst / visualization lead who guides client discovery, translates user requirements, and directs the overall user experience (UI/UX) and visualization approach. The Data Engineer & Analytics Developer is responsible for executing the technical work: engineering the data, analyzing it, and implementing and refining the visualization deliverables under that guidance. Successful candidates can work alongside the business analyst / visualization lead to go from raw data to a functioning end-user solution, applying sound technical and analytical judgment to produce reliable, maintainable outputs and meaningful insights.
Key Responsibilities
Data Engineering, Integration & Quality | Primary Focus
Investigate and profile source data, including databases, spreadsheets, APIs, GIS datasets, files, and cloud-based systems; identify access, structure, quality, and integration challenges.
Design, build, document, and maintain repeatable ETL/ELT pipelines that extract, combine, clean, standardize, and transform data from disparate sources.
Write and optimize SQL queries and use programming or scripting to automate data preparation, transformation, validation, and recurring workflows.
Develop and maintain logical data models, schemas, and structured datasets suitable for analysis, reporting, dashboards, and geospatial applications.
Implement data-quality checks, reconciliation, and exception handling for missing, duplicated, inconsistent, or erroneous records; verify results with project subject matter experts.
Troubleshoot data issues, improve reliability and performance, and maintain clear documentation of source-to-output transformations, assumptions, and dependencies.
Evaluate technical feasibility, dependencies, and implementation effort and provide practical data-engineering solutions in coordination with the project and business analysis leads.
Apply sound data management practices throughout the data lifecycle, including consistent naming and organization, metadata standards and data dictionaries, documentation of data sources, transformations and lineage, identification of authoritative and derived datasets, and capture of important assumptions, limitations, and intended uses.
Use version control practices to manage code, scripts, configuration, and other technical assets so that analytical workflows are traceable, reproducible, and maintainable.
Data Analysis & Insight Development
Perform exploratory and quantitative analysis to identify patterns, trends, anomalies, relationships, and findings that address project questions.
Apply an understanding of data provenance, domain context, and analytical limitations to distinguish meaningful insights from misleading or incomplete results.
Develop analysis-ready datasets, calculations, summaries, and clean outputs that support decision-making and downstream applications.
Work with technical experts and the business analyst / visualization lead to interpret results, validate conclusions, and help explain the significance and practical implications of findings.
Dashboard, Visualization & Analytical Tool Execution
Build, configure, test,…