Customer Analytics Engineer

Department: Data

Employment Type: Full Time

Location: Remote

Grasshopper Bank is a client-first, digital bank built for the business and innovation economy, with an obsession for providing leading-edge technology, solutions-driven products and superior service through a combination of passionate people and digital resources.

We are a highly experienced team who pride ourselves on diversity of thought and perspective. Nationally chartered, our portfolio of products and services range from VC, PE & Portfolio Companies to SBA Lending, as well as direct SMB and Embedded Banking.

Our entrepreneurial drive allows us to support the growth and success of a wide range of clients at every stage of their business through inclusive partnership. We seek out team members who will enable both our organization and our people to grow and thrive through collaboration and acting with integrity and respect.

Our focus on cross-functional teamwork provides a culture where ideas are valued, accountability is encouraged, and successes are celebrated. We welcome all those searching for the opportunity to contribute to banking innovation that influences and supports the emerging digital world of Financial Services.

Our digital first approach enables our teams the flexibility to work remotely. We have offices in NYC and Boston.

What you'll do:

As a Customer Analytics Engineer, you will operate at the intersection of modern data engineering, customer behavior analytics, and growth enablement. Reporting to the Director of BI & Analytics, you will serve as the technical engine and strategic architect behind our bank-wide Customer 360 and customer lifecycle analytics initiatives.

In this high-impact role, you will move beyond standard descriptive reporting to build the foundational data models, pipelines, and semantic layers that explain who our clients are, how they interact with our digital banking platforms, and what drives long-term customer lifetime value (LTV). You will be responsible for modeling complex customer event streams, digital touchpoints, and transaction histories into analytics-ready models in Google BigQuery, powering executive Tableau reporting and directly activating customer data downstream into marketing and engagement engines (such as Braze).

If you love writing clean, performant SQL/Python transformation logic, unraveling complex customer journeys, and partnering with Product and Marketing leaders to unlock top-line revenue, this role is for you.

What you need:

• Experience: 2–4+ years of hands-on experience in a hybrid data role such as Analytics Engineer, Customer Data Engineer, BI Developer, or Marketing Data Engineer. Prior experience in fintech, commercial banking, or digital banking platforms is preferred.
• Advanced SQL & BigQuery: Expert-level SQL skills, including advanced analytical window functions, complex CTEs, array/struct unnesting, and performance tuning within cloud data warehouses (Google BigQuery strongly preferred).
• Dimensional & Event Data Modeling: Strong experience with dimensional modeling, star schemas, and structuring behavioral/event-based data models (hands-on experience with dbt for modeling transformations and lineage is a strong plus).
• Python & Pipeline Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation. Experience scheduling and orchestrating DAGs using tools like Apache Airflow or Google Cloud Composer is preferred.
• Tableau Expertise: Deep proficiency in Tableau Desktop and Tableau Server/Cloud, including Level of Detail (LOD) expressions, parameters, and thoughtful dashboard UX/UI design principles.
• Customer Analytics Acumen: Strong conceptual understanding of customer lifecycle metrics: Customer Acquisition Cost (CAC), Lifetime Value (LTV), cohort retention, and churn. Familiarity with reverse-ETL workflows or customer data activation is a plus.
• Communication & Collaboration: Proven track record of presenting complex data insights to non-technical stakeholders and driving cross-functional alignment.
• Education: Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Mathematics, Engineering, or equivalent practical experience.