Customer Analytics Engineer

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.

Customer 360 & C-MDM Modeling: 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.
Identity Resolution, Governance & Attribution: 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.
Semantic Layer & Master Data Development: 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.
Scalable ELT Pipelines: 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.
Downstream Marketing & Activation Sync: 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.
Data Quality & Observability: Implement automated testing, anomaly detection, and schema validation across customer data pipelines to guarantee platform accuracy, uptime, and alerting.
Customer Journey & Cohort Dashboards: Design and maintain intuitive, executive-ready Tableau dashboards focusing on customer acquisition funnels, retention curves, product penetration, churn indicators, and deposit velocity.
Storytelling with Data: Translate complex, multi-layered data into clear, actionable narratives that answer the "so what?" for business line leaders and product managers.
Performance Optimization: Continuously monitor and tune BigQuery queries and Tableau extracts to ensure rapid workbook performance and cost-efficient query processing.
Strategic Growth Partner: Collaborate closely with Marketing, Product, and Business Line leaders to translate business hypotheses into quantitative frameworks and tracking requirements.
Data Stewardship & Literacy: 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.
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.
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 Kimball dimensional modeling, star schemas, and structuring behavioral/event-based data models.
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.
Python for Data Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation.
Customer Analytics Acumen: Strong conceptual understanding of customer lifecycle metrics: Customer Acquisition Cost (CAC), Lifetime Value (LTV), cohort retention, churn hazard rates, and marketing attribution funnels.
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.