Customer Analytics Engineer

Grasshopper Bank is a digital bank serving businesses and organizations in the innovation economy. The Customer Analytics Engineer builds customer data models, pipelines, and analytics that support Customer 360 and lifecycle initiatives, executive reporting, and customer engagement activation. The role partners with Product, Marketing, and business leaders to turn customer data into insights and growth opportunities.

Responsibilities:

• Design, build, and optimize scalable dimensional models (Kimball methodology) and One Big Table (OBT) datasets in Google BigQuery that unify core banking records, digital session logs, and touchpoints into a trusted, bank-wide customer master (C-MDM) and single source of truth
• Establish robust customer identity mapping and multi-touch marketing attribution models across diverse platforms, while championing master data management (C-MDM) principles, data quality, and governance across all customer data pipelines
• Formalize bank-wide customer entities, core segmentations, and Critical Data Elements (CDEs)—such as Active User definitions (DAU/MAU), primary operating account status, feature adoption, and dormancy thresholds—to align business and technical definitions under a unified C-MDM framework
• Design, deploy, and maintain resilient ELT/ETL pipelines (using SQL, Python, and orchestration tools) to ingest, clean, and enrich raw customer interaction data, API payloads, and core snapshot tables
• Engineer reliable pipelines and reverse-ETL workflows that sync enriched customer traits, behavioral events, and engagement tiers directly into marketing automation platforms (e.g., Braze) and CRM systems to power automated lifecycle campaigns
• Implement automated testing, anomaly detection, and schema validation across customer data pipelines to guarantee platform accuracy, uptime, and alerting
• Design and maintain intuitive, executive-ready Tableau dashboards focusing on customer acquisition funnels, retention curves, product penetration, churn indicators, and deposit velocity
• Translate complex, multi-layered data into clear, actionable narratives that answer the "so what?" for business line leaders and product managers
• Continuously monitor and tune BigQuery queries and Tableau extracts to ensure rapid workbook performance and cost-efficient query processing
• Collaborate closely with Marketing, Product, and Business Line leaders to translate business hypotheses into quantitative frameworks and tracking requirements
• Champion data governance best practices, document customer data lineages and KPI definitions in the Data Steward Playbook, and coach end-users on self-service data discovery

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