Full-Cycle Data Engineer

About The Role

We're looking for a Full-Cycle Data Engineer to join our Data & AI team and own the flow from product data sources → modeling → dashboards → insights. You'll partner with product managers, engineers, and AI teams to turn raw product data into reliable analytics infrastructure that drives decisions across the company — from individual feature bets to CEO- and CFO-level questions.

This is an end-to-end role: you'll take data products from ideation through engineering, analytics, and production deployment.

Responsibilities

Pipelines & infrastructure

• Design, build, and deploy scalable data pipelines from product and system sources in production, using Python and orchestrators like Airflow.
• Work with distributed query engines such as BigQuery or Athena, with strong SQL throughout.
• Build and maintain semantic data models for large-scale operational systems and data lakes, manually or with tooling like dbt.
• Improve the end-to-end analytics stack, from ingestion to visualization, and collaborate with engineering on event tracking and instrumentation.
• Ensure data quality, consistency, and reliability across the stack.
  
  

Analytics & reporting

• Build and maintain dashboards and reporting layers in tools like Looker or Metabase, optimized for performance, usability, and clarity.
• Create self-serve analytics so product and business stakeholders can answer their own questions.
• Support product experimentation: A/B testing, funnel analysis, feature adoption.
  
  

Partnership & insight

• Translate ambiguous questions from product leads, the CEO, the CFO, and others into clear metrics, KPIs, and analytical models.
• Surface trends in usage and user behavior that influence the product roadmap and feature prioritization.
• Provide ad-hoc analysis and strategic reporting for leadership.
  
  

Requirements

• 5+ years in data engineering, data analytics, or product analytics.
• Strong SQL and hands-on experience with large-scale datasets in cloud data warehouses (BigQuery or similar).
• Production Python experience for data pipelines.
• Solid grounding in product metrics, funnels, and user behavior analysis.
• Ability to turn business questions into data models, metrics, and dashboards.
• Strong communication and cross-functional collaboration skills.
  
  

Nice to Have

• Streaming or event-driven data systems.
• Product instrumentation and tracking design.
• AI/ML or LLM experience.
• High-scale SaaS or consumer product environments.
  
  

About April

april is the only embedded, year-round tax platform built to power smarter financial decisions. From filing to planning to onboarding, april’s white-labeled tools bring real-time tax intelligence into the platforms people already use, helping users understand the impact of every paycheck, equity transaction, or income shift, and stay on top of tax payments throughout the year. Built to handle even the most complex tax situations, april’s AI-powered tax engine ingests data directly from partner apps to deliver accurate outcomes in record time—making tax planning and filing more connected, contextual, and accessible than ever. With API-first infrastructure and seamless data integrations, april helps partners deliver more value, deepen loyalty, and turn taxes into a strategic edge—for their clients and their business.