Customer Analytics Engineer

Grasshopper Bank•New York, NY

6d•Remote

About The Position

Grasshopper Bank is hiring a Customer Analytics Engineer to work at the intersection of modern data engineering, customer behavior analytics, and growth enablement. Reporting to the Director of BI & Analytics, this role will serve as the technical engine and strategic architect for bank-wide Customer 360 and customer lifecycle analytics initiatives. The position builds foundational data models, pipelines, and semantic layers to explain customer identity, interactions with digital banking platforms, and drivers of customer lifetime value. It models customer event streams, digital touchpoints, and transaction histories into analytics-ready Google BigQuery models, powers executive Tableau reporting, and activates customer data downstream in marketing and engagement platforms such as Braze. The role partners with Marketing, Product, and business-line leaders to turn business hypotheses into quantitative frameworks and tracking requirements. It also advances customer data quality, governance, observability, performance, documentation, and self-service data literacy. The company describes its approach as digital-first and says teams have flexibility to work remotely, with offices in New York City and Boston.

Requirements

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.
Expert-level SQL skills, including analytical window functions, complex CTEs, array/struct unnesting, and cloud data warehouse performance tuning.
Strong experience with dimensional modeling, star schemas, and behavioral or event-based data models.
Demonstrated experience using Python for data transformation, API integrations, and workflow automation.
Deep proficiency in Tableau Desktop and Tableau Server or Cloud, including Level of Detail expressions, parameters, and dashboard UX/UI design principles.
Strong conceptual understanding of customer lifecycle metrics, including CAC, LTV, cohort retention, and churn.
A proven ability to present complex data insights to non-technical stakeholders and drive cross-functional alignment.
Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Mathematics, Engineering, or equivalent practical experience.

Nice To Haves

Prior experience in fintech, commercial banking, or digital banking platforms.
Hands-on experience with dbt for modeling transformations and lineage.
Experience scheduling and orchestrating DAGs using Apache Airflow or Google Cloud Composer.
Familiarity with reverse-ETL workflows or customer data activation.
Google BigQuery experience is strongly preferred.

Responsibilities

Design, build, and optimize scalable dimensional models using Kimball methodology and One Big Table datasets in Google BigQuery, unifying banking records, digital session logs, and touchpoints into a trusted customer master and single source of truth.
Establish customer identity mapping and multi-touch marketing attribution models, and champion customer master data management principles, data quality, and governance across customer data pipelines.
Formalize customer entities, segmentations, and Critical Data Elements—including Active User definitions, primary operating account status, feature adoption, and dormancy thresholds—to align business and technical definitions.
Design, deploy, and maintain resilient ELT/ETL pipelines using SQL, Python, and orchestration tools to ingest, clean, and enrich customer interaction data, API payloads, and core snapshot tables.
Build reliable pipelines and reverse-ETL workflows that sync enriched customer traits, behavioral events, and engagement tiers into marketing automation platforms and CRM systems.
Implement automated testing, anomaly detection, and schema validation across customer data pipelines to support accuracy, uptime, and alerting.
Design and maintain executive-ready Tableau dashboards covering acquisition funnels, retention curves, product penetration, churn indicators, and deposit velocity.
Translate complex data into clear, actionable narratives for business-line leaders and product managers.
Monitor and tune BigQuery queries and Tableau extracts for workbook performance and cost-efficient query processing.
Collaborate with Marketing, Product, and business-line leaders to translate business hypotheses into quantitative frameworks and tracking requirements.
Promote data governance practices; document customer data lineage and KPI definitions in the Data Steward Playbook; and coach end users on self-service data discovery.