Data Analytics Engineer

STEAMe LLC•Chicago, IL

18h•Hybrid

About The Position

As a Data Analytics Engineer at STEAMe, you will sit at the intersection of analytics, data engineering, and emerging AI-powered workflows. You’ll be responsible for building reliable data pipelines, transforming data for analysis, and delivering high-quality dashboards and reports that drive product, operational, and customer insights. Working closely with product, engineering, customer success, and business stakeholders, you’ll own data transformations, support analytics architecture, and help evolve how STEAMe uses data — including experimenting with LLM-assisted analysis and prompt-based workflows to surface insights more efficiently. This is an excellent opportunity for someone who enjoys working across the full data lifecycle — from ingestion and modeling to visualization and insight delivery, — in a fast-paced startup environment.

Requirements

Bachelor’s degree in Analytics, Computer Science, Engineering, Statistics, Economics, Mathematics, or a related field (or equivalent experience)
3–6 years of experience in analytics, analytics engineering, or a data engineering–adjacent role
Strong proficiency in SQL and Python
Experience building and maintaining data pipelines, transformations, or ETL processes
Hands-on experience with BI and visualization tools (e.g., Tableau, Looker, Power BI)
Solid understanding of data modeling concepts and analytics best practices
Ability to communicate clearly with both technical and non-technical stakeholders
Comfortable working in a fast-moving startup environment with evolving requirements

Nice To Haves

Experience with cloud data platforms (e.g., AWS, GCP, or Azure)
Familiarity with modern analytics stacks (e.g., dbt or similar transformation tools)
Experience working with SaaS platforms or in edtech / workforce development environments
Exposure to data orchestration tools and APIs
Experience using or designing LLM-powered analytics workflows or prompt-based tools

Responsibilities

Partner with cross-functional teams to understand business objectives and translate them into scalable data models, metrics, and analytics solutions
Design, build, and maintain data transformations and lightweight ETL pipelines using SQL and Python
Develop and maintain curated analytics tables and semantic layers to support reporting and dashboards
Create and manage dashboards and reports in BI tools (e.g., Tableau, Looker, Power BI) for internal teams and external partners
Ensure data accuracy, consistency, and reliability across analytics outputs
Support and evolve data architecture by collaborating with engineering on source systems, data flows, and integrations
Write Python scripts for data preparation, automation, and analytics workflows
Experiment with and develop LLM-enabled workflows, including prompt design, to extract insights, summarize data, or support internal analytics use cases
Experiment with AI tools to deliver new methodologies and optimize workflows
Document data models, pipelines, metrics definitions, and analytics best practices
Troubleshoot data quality issues and proactively identify opportunities to improve data processes and performance