Senior Analytics Engineer

Shippo

25d•Remote

About The Position

We are seeking a Senior Analytics Engineer to partner with leadership to drive strategy and build scalable data infrastructure. This role involves collaborating with product, operations, and go-to-market leaders to identify impactful problems, translate business questions into analytical frameworks, and present insights to executives. You will own key business metrics and KPIs, design and maintain dimensional data models using dbt, and create self-service dashboards and reporting tools in Looker/Databricks. Additionally, you will establish data quality standards, operationalize analytical workflows, and contribute to high-impact analytical initiatives. Depending on the business unit, responsibilities may include designing growth experiments, building fraud detection models, optimizing conversion funnels, creating partner health scorecards, developing carrier variance models, and improving operational efficiency. You will also act as a multiplier for your team by reducing ad-hoc requests, mentoring others on analytical thinking, and contributing to company-wide data platform initiatives.

Requirements

6+ years of experience in analytics engineering, data engineering, data science, product analytics, or related roles with increasing scope and impact
Expert SQL skills: You write complex, performant queries and deeply understand data modeling principles and optimization techniques
Analytics engineering expertise: Proven experience building and maintaining data transformation pipelines with dbt or similar tools.
Data visualization proficiency: Strong experience with Looker or similar BI platforms (Tableau, Mode, Hex) creating dashboards that drive action, not just display data.
Modern data stack knowledge: Hands-on experience with cloud data warehouses such as Databricks and relevant AWS technologies for big data & analytics.
Business acumen and communication: You translate technical findings into clear narratives that resonate with both technical and non-technical audiences. You can influence without authority and build trust across functions
Proven impact: Clear examples of analytical work that led to measurable business outcomes (e.g revenue growth, cost savings, improved conversion rates, or operational efficiency gains)
Strategic thinking: You don't just execute requests; you proactively identify opportunities, ask clarifying questions, and recommend the best path forward
Autonomy and ownership: You're comfortable working independently, managing ambiguity, and seeing complex projects through from conception to adoption

Nice To Haves

Experience with Python for statistical analysis, data manipulation, and light scripting
Background working with product, growth, or operations teams in high-growth tech companies or startups
Domain knowledge in shipping, logistics, marketplaces, payments, or API-first products
Experience with experimentation and A/B testing frameworks
Familiarity with fraud detection, risk modeling, or anomaly detection
Previous experience in a "hub and spoke" or embedded analytics model
Hands-on experience building customer-facing or internal data products

Responsibilities

Partner with leadership to drive strategy
Collaborate with product, operations, and go-to-market leaders to identify the most impactful problems to solve and translate ambiguous business questions into structured analytical frameworks
Present insights and recommendations to executives and influence strategic decisions across your business unit
Develop a deep understanding of your domain's challenges, competitive landscape, and growth opportunities
Own key business metrics and KPIs: defining, instrumenting, and evangelizing them across stakeholders
Design, build, and maintain dimensional data models using dbt that serve as the foundation for analytics across your domain
Create self-service dashboards and reporting tools in Looker/Databricks that empower teams to answer their own questions
Establish data quality standards and monitoring to ensure stakeholders can trust the data
Operationalize analytical workflows that scale beyond one-time analyses—building reusable frameworks, automated monitoring, and sustainable data products
Design and analyze growth experiments, build fraud detection models, optimize conversion funnels, and measure product feature adoption to drive user growth and reduce losses (App specific)
Create partner health scorecards, analyze API usage patterns, monitor integration reliability, and identify expansion opportunities to strengthen partnerships and operational visibility (API specific)
Develop carrier variance and cost attribution models, track SLA performance, identify margin leakage, and improve operational efficiency to recover costs and drive accountability (Carrier Operations specific)
Reduce ad-hoc data requests by building reusable data products and clear documentation that enable self-service
Mentor product managers and operators on analytical thinking and data literacy
Contribute to Data Platform initiatives that improve analytical capabilities company-wide
Share knowledge and best practices across the analytics community at Shippo