Key Responsibilities
As a Backend Data Engineer, your primary objective is to build and maintain the lifeblood of Neighborhoods—its data. You will be responsible for designing pipelines that ingest data from external APIs and internal sources, ensuring it flows efficiently into Snowflake or other data stores.
You will collaborate closely with the wider engineering team to assist with database design and data flows, acting as a technical consultant for data-related problems. Beyond the code, you are expected to develop documentation and engineering best practices that make the team more efficient. By the 90-day mark, you are expected to be contributing to data architecture decisions, and by 6 months, you should be owning multiple data streams that span across different teams.
Role Requirements & Qualifications
A successful candidate for this role is someone who has "been there, done that" regarding data infrastructure.
- Must-have skills:
- 2–5 years of experience in Data Engineering or Backend Engineering.
- Expert-level proficiency in SQL and at least one primary programming language (e.g., Python).
- Demonstrated experience in building and maintaining high-volume ETL systems.
- Nice-to-have skills:
- Experience with AWS cloud environments.
- Proficiency with dbt, Airbyte, or BI tools like Tableau or Amazon QuickSight.
- An advanced degree in Computer Science or related fields.
Frequently Asked Questions
Q: Is the interview process difficult?
A: Candidates generally describe the difficulty as "average." While the technical requirements are high, the style is conversational and collaborative rather than adversarial or purely focused on "trick" questions.
Q: How much time should I spend preparing?
A: Dedicate significant time to reviewing your own past projects. Since the interview is deeply rooted in your history and your approach to architecture, being able to articulate your past successes clearly is more valuable than cramming for coding tests.
Q: What is the company culture like?
A: Neighborhoods values inclusivity and collaboration. They look for team players who are curious, self-motivated, and comfortable working in a remote, contractor-friendly environment.
Other General Tips
- Own your mistakes: When asked about a time you failed, be honest and focus on what you learned. This demonstrates self-awareness, a quality highly valued by the Neighborhoods leadership.
- Focus on the "Why": In technical discussions, don't just state what tools you used; explain why they were the right fit for the specific problem you were solving.
- Prepare your questions: Use the final part of your interviews to ask about the current data challenges the team is facing. This shows you are already thinking like a member of the team.