Customer Analytics Engineer

This is a fully remote position, open to applicants in New York.

📋 Description

• Design, construct, and enhance scalable datasets for Customer 360, C-MDM, dimensional, and One Big Table within Google BigQuery.

• Establish practices for customer identity mapping, multi-touch marketing attribution, data quality, and governance.

• Define customer entities, segmentations, Critical Data Elements, along with unified business and technical definitions.

• Create, implement, and sustain robust ELT/ETL pipelines utilizing SQL, Python, and orchestration tools.

• Develop reverse-ETL workflows that synchronize customer traits, behavioral events, and engagement tiers with Braze and CRM systems.

• Incorporate automated testing, anomaly detection, schema validation, uptime monitoring, and alerting mechanisms.

• Design and manage Tableau dashboards focused on acquisition funnels, retention, product penetration, churn, and deposit velocity.

• Transform intricate data into actionable narratives for business leaders and product managers.

• Enhance BigQuery queries and Tableau extracts for performance optimization and cost efficiency.

• Collaborate with Marketing, Product, and Business Line leaders on quantitative frameworks and tracking requirements.

• Document customer data lineages and KPI definitions, while training end-users on self-service data discovery.

⛳️ Requirements

• 2–4+ years of practical experience in a hybrid data role such as Analytics Engineer, Customer Data Engineer, BI Developer, or Marketing Data Engineer.

• Proficient SQL skills, including advanced analytical window functions, complex CTEs, array/struct unnesting, and performance tuning within cloud data warehouses.

• Extensive experience with Google BigQuery.

• Strong background in Kimball dimensional modeling, star schemas, and behavioral/event-based data models.

• In-depth expertise in Tableau Desktop and Tableau Server/Cloud, including LOD expressions, parameters, and dashboard UX/UI design principles.

• Proven experience using Python for data transformation, API integrations, and workflow automation.

• Solid understanding of CAC, LTV, cohort retention, churn hazard rates, and marketing attribution funnels.

• Experience in presenting complex data insights to non-technical stakeholders and facilitating cross-functional alignment.

• Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Mathematics, Engineering, or equivalent practical experience.

🏝️ Benefits

• Flexibility to work remotely.

• Emphasis on diversity of thought and perspective.

• Culture of cross-functional teamwork.

• Opportunities to engage in banking innovation.

• Supportive partnership and collaborative growth environment.