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NerdWallet is seeking a Data Engineer II to design, develop, and maintain data systems and pipelines that power analytics, experimentation, and strategic decision-making across the organization. You'll work within a product vertical, directly shaping the data infrastructure that drives business decisions, product innovation, and user experiences in the financial services space.
In this role, you'll lead well-scoped data assets—ensuring they are accurate, reliable, documented, and aligned with stakeholder needs. You'll build and improve pipelines using established patterns, focusing on maintainability, scalability, and quality. You'll solve moderately complex problems independently while knowing when to escalate for broader input on tradeoffs and dependencies.
Key responsibilities include strengthening pipeline reliability and observability by monitoring jobs, investigating failures, improving tests, and preventing recurrence. You'll communicate clearly with cross-functional partners on requirements, assumptions, and progress. You'll break down ambiguous work, ask thoughtful questions, and partner with senior engineers on technical direction. You'll prioritize by impact, urgency, and risk, communicating tradeoffs as scope or timing shifts.
You'll write clean, tested, documented code while adopting modern practices including AI-enabled tooling. Over time, you'll grow technical depth by taking on harder workstreams and learning from feedback, reviews, and mentorship.
Required experience includes 3+ years in data engineering, analytics engineering, software engineering, or related fields. You should be hands-on with modern data stack tools such as AWS, Snowflake, dbt, Airflow, and Databricks. Strong SQL proficiency and working Python skills for pipeline development, transformation, automation, and debugging are essential. You should be comfortable with AI-assisted tools like Cursor or GitHub Copilot, have working knowledge of relational databases and data modeling, and experience building and troubleshooting production pipelines. Familiarity with data quality, monitoring, logging, testing, and observability is required. A Bachelor's or Master's in Computer Science, Engineering, or related field (or equivalent experience) is preferred.
