Analytics Engineer

FiscalNote

18d

About The Position

At FiscalNote, we build platforms that connect people to their governments, and our Data Analytics team builds tools, processes, and reporting capabilities to integrate operational data and enable enterprise decision making. As an Analytics Engineer on the Data Analytics team, you recognize the value of properly managing, analyzing and reporting data to improve operational efficiencies, customer experience, and financial performance, and want to build the data models and analytics capabilities that empower decision making across the company. You will work closely with our product and operations teams to build clean, scalable analytics models which are the backbone of our reporting and advanced analytics capabilities. This role reports to the Director of Data Analytics & Business Intelligence.

Requirements

4+ years in analytics engineering, data analysis, or a related technical role
Excellent data modeling proficiency with an eye toward simplicity and modularity
Advanced proficiency in SQL, including complex joins, window functions, and CTEs
Production experience in at least one dbt project
Strong dimensional, relational, or semantic data modeling skills
Familiarity with structured, semi-structured and unstructured data formats
Experience with data visualization products such as Metabase, Looker, and Tableau
Ability to explore and analyze complex datasets
Strong communication skills and ability to write thorough, high-quality documentation
Basic statistical analysis to identify trends, outliers, etc.
Git-based development, pull requests, code review, and CI/CD

Nice To Haves

Experience with AI and agentic harnesses is a plus but not required for this role.
Experience with Snowflake and SnowSQL (preferred)
Experience with Metabase (preferred)
Python proficiency (preferred)
Experience performing data munging and analysis with tools and environments like Pandas/Polars and Jupyter Notebooks (preferred)

Responsibilities

Build and maintain dbt models with SQL and Jinja for analytics and dataops across departments including finance, product, sales, and customer success
Harden analytics models via tests, freshness checks, constraints, etc. to ensure the quality and correctness of our data
Create reports and dashboards in Metabase using the visual query builder
Conduct ad-hoc analysis to understand our data in depth and answer business questions
Collaborate with stakeholders across the organization to understand their respective business domains and design analytics solutions for them