Analytics Engineer
Civitech
Description
Civitech is a public benefit corporation dedicated to creating a fairer and more equitable democracy by building the tools and infrastructure needed to increase civic participation, empower Democratic candidates to win, and support the success of progressive causes. Since its founding in 2019, over 500 partners -- a range of nonprofit organizations, national political committees, and individual campaigns -- have utilized Civitechâs tools to reach tens of millions of voters to help create a more equitable and progressive democracy.
Civitech is a remote-first company hiring within our current footprint of 27 states (AL, AK, CA, CO, DC, DE, FL, GA, HI, IL, MA, MD, MN, NC, ND, NH, NJ, NV, NY, OH, SD, TN, TX, VA, WA, WI, WY); Civitech does have an office in Austin, TX.
It is important that our team reflects the diversity of the organizations we seek to serve. We strongly encourage women, people of color, LGBTQIA+ people, and others otherwise underrepresented in the technology sector to apply.
\n- We act with Integrity â At Civitech, we hold ourselves to the highest standards and value open and transparent communications with all of our stakeholders. Our rigorous approach to product design, testing, and data science leads to accurate assessments of our outcomes and challenges us to constantly improve our tools.
- We are Changemakers â As a team, Civitech seeks to make transformational change in our democracy by eliminating obstacles meant to hamper contribution from every member of the community.
- We are Collaborators - Buoyed by our mission, we look for opportunities to partner with everyone committed to making democracy easier to participate in. We seek to understand the challenges our partners face and use our skills and creativity to help them solve them.
- We are Bold â We recognize that disruptive change won't come with doing business as usual. Civitech seeks to revolutionize civic participation by bringing innovation and creativity to politics.
- Build and maintain dbt models that transform raw data from multiple sources into clean, tested, well-documented datasets.
- Partner with data scientists, engineers, and product teams to translate ambiguous questions into durable data models rather than one-off queries.
- Improve data quality across the stack by writing tests, defining expectations for critical models, and triaging issues when something looks wrong.
- Document models, metrics, and lineage so engineers, analysts, and partner organizations can self-serve with confidence.
- Use Python where it's the right tool â for orchestration, ad-hoc work, or transformations that don't belong in dbt.
- Raise the bar on analytics engineering practices, including code review, modeling conventions, CI for data, and documentation standards.
- Perform additional engineering and data duties as needed to support the broader team.
- 3+ years of experience in analytics engineering, data engineering, or a closely related role where modeling data was central to the job.
- Strong SQL skills and production experience with dbt, including tests, documentation, and a sensible approach to project structure.
- Comfortable in Pyth
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About Civitech
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Interview Prep Guide
Preparation Strategy
To prepare for this role, focus on reviewing dbt fundamentals, data modeling best practices, and data pipeline design principles. Practice writing dbt models and be prepared to whiteboard solutions to common data quality issues. Also, review Civitech's values and be prepared to give specific examples of how you've demonstrated those values in your previous work. Finally, be prepared to talk about your experience working with data quality issues and how you've improved data quality in your previous roles.
Likely Interview Rounds
- 1. Screening call~30 min
What to prep: Review dbt fundamentals, data modeling best practices, and be prepared to talk about your experience working with data quality issues.
- What experience do you have with data modeling and dbt?
- How do you ensure data quality in your work?
- Can you describe a time when you had to translate ambiguous questions into durable data models?
- 2. Technical~60 min
What to prep: Practice writing dbt models, review data pipeline design principles, and be prepared to whiteboard solutions to common data quality issues.
- How would you design a data pipeline to handle raw voter data?
- What are some common data quality issues you've encountered in the past and how did you resolve them?
- Can you write a simple dbt model to transform a sample dataset?
- 3. Behavioral~60 min
What to prep: Review Civitech's values and be prepared to give specific examples of how you've demonstrated those values in your previous work.
- Can you describe a time when you had to collaborate with a cross-functional team to solve a data-related problem?
- How do you handle conflicting priorities and tight deadlines in your work?
- Can you tell me about a project you worked on that involved improving data quality or documentation?
Most Likely Questions
- What experience do you have with data modeling and dbt?
- How do you ensure data quality in your work?
- Can you describe a time when you had to translate ambiguous questions into durable data models?
- How would you design a data pipeline to handle raw voter data?
- What are some common data quality issues you've encountered in the past and how did you resolve them?
Common Pitfalls
- Lack of experience with dbt or data modeling
- Inability to communicate technical concepts to non-technical stakeholders
- Insufficient attention to data quality and documentation
Free Prep Resources
- • dbt Documentation
- • Data Pipeline Design Principles (Towards Data Science)
- • Data Quality Best Practices (Data Quality Toolbox)