Staff Data Analytics Engineer

CareerPlug

6d•Remote

About The Position

CareerPlug's mission is to help franchises build winning teams by providing a systematic approach to hiring and retention through their innovative recruiting and HR software. They are committed to being a great place to work, emphasizing care, purpose, development, and their core values. CareerPlug is an equal opportunity employer focused on fostering a diverse and inclusive environment. This Staff Data Analytics Engineer role is central to the data stack, responsible for transforming raw data into modeled, tested, and documented datasets for business and product teams. As the sole dedicated data engineer and the most senior technical person in the data function, this role will collaborate with the BI team and the Product & Engineering function. Key responsibilities include setting patterns for analytics work, defining core metrics, and creating stable interfaces between the data warehouse and the Rails application. The role will also involve evaluating and potentially leading a transition away from a proprietary BI platform, establishing data definitions, tests, contracts, pipelines, and ensuring data trustworthiness. This is not a cloud infrastructure role; the focus is on data architecture, transformations, contracts, and software practices above the infrastructure layer.

Requirements

Demonstrated experience designing and maintaining production data pipelines and transformation layers.
Strong SQL and relational data modeling experience.
Experience with automated testing, version control, code review, and software engineering practices applied to analytics/data work.
Experience defining or governing shared metrics, semantic concepts, or trusted datasets used across teams.
Experience integrating data systems with applications, APIs, or operational workflows.
You’re strongest in the middle of the data stack: transforming raw data into reliable, well-modeled datasets and interfaces that others can build on.
You have deep SQL skills and strong judgment around data modeling, testing, documentation, version control, and production data quality.
You’ve worked with modern data and BI ecosystems and can evaluate tools based on the problem they solve rather than attachment to a particular vendor.
You’re comfortable leading through ambiguity and can explain technical tradeoffs clearly.
You think like a software engineer about data: code quality, maintainability, contracts, tests, incremental changes, and future maintainers all matter to you.
You can move between technical and business conversations and translate business meaning into durable data definitions.
You enjoy being a senior individual contributor who influences through technical judgment, collaboration, mentoring, and standards rather than people management.
You use AI-assisted engineering intentionally without lowering the bar for what ships.

Nice To Haves

Experience participating in or leading a BI/data tooling migration is strongly preferred.
Experience with Ruby on Rails or another backend application framework is a plus.
Experience with Domo or another proprietary BI platform is helpful, but not required.

Responsibilities

Own the architecture and reliability of data pipelines that bring complete, timely data into the warehouse.
Build and maintain modeled, tested, documented datasets that teams can trust and safely change.
Define and steward core business metrics and entities so important numbers have one clear, discoverable definition.
Create and maintain stable data contracts between the warehouse and our Rails application.
Design data quality practices that catch issues early, including testing, monitoring, freshness expectations, and incident follow-up.
Own and improve operational data writebacks and system-to-system syncs.
Partner with BI teammates and business stakeholders to understand the business domain behind the data.
Set technical direction for our data function, weighing architecture and tooling decisions for maintainability, cost, scalability, and future needs.
Write and review production-quality code, documentation, and pull requests that other engineers can safely extend.
Use AI coding tools thoughtfully while maintaining accountability for quality, security, testing, and maintainability.