Data Insights and Analytics Engineer

1 Month ago • 5 Years + • Data Analyst

Job Summary

Job Description

The Data Insights & Analytics Engineer transforms extensive commerce data into actionable intelligence driving strategic decision-making. Responsibilities include designing analytics solutions and dashboards, building reporting frameworks, developing payment analytics and fraud detection models, creating compelling data narratives, and collaborating with stakeholders. The role requires transforming complex data into business insights, optimizing marketing investments, and ensuring data accuracy. This involves leveraging Apache Spark, Databricks, SQL, Python/R, and BI tools like AWS Quicksight, Tableau, Power BI, or Looker. Experience with commerce, payments, or subscription businesses is crucial.
Must have:
  • 5+ years data analytics experience
  • Proficiency in Apache Spark & Databricks
  • Strong SQL & Python/R skills
  • BI tool experience (e.g., AWS Quicksight)
  • Knowledge of financial metrics & KPIs
  • Data storytelling & presentation skills
Good to have:
  • Master's degree in relevant field
  • Experience with commerce platforms
  • Marketing analytics experience
  • Machine learning experience
  • Financial reporting experience

Job Details

The Data Insights & Analytics Engineer transforms the Unified Commerce Platform’s (UCP) extensive commerce data into actionable intelligence that drives strategic decision-making across the organization and for our external partners. This role goes beyond standard reporting to uncover critical patterns in payment behaviors, fraud indicators, revenue optimization opportunities, and customer engagement. The ideal candidate will develop compelling data narratives that illuminate business performance, identify emerging risks, optimize marketing effectiveness, and enhance the overall commerce ecosystem for our diverse tenant base.

What you'll be doing:

  • Design and develop comprehensive analytics solutions and interactive dashboards for subscription metrics, payment performance, fraud patterns, and revenue optimization

  • Build robust reporting frameworks with scalable solutions and customizable templates for internal and external stakeholders

  • Develop payment analytics, fraud detection models, chargeback analysis frameworks, and revenue leakage detection systems

  • Transform complex data into compelling narratives through executive-level presentations and self-service documentation

  • Create attribution analytics, customer segmentation models, and lifecycle analytics to optimize marketing investments

  • Collaborate with cross-functional stakeholders to translate business questions into technical analytics requirements

  • Research and implement advanced analytics techniques while staying current with industry trends

  • Establish data validation frameworks and monitoring systems to ensure the accuracy of financial and behavioral metrics

What we want to see:

  • Bachelor's degree in Data Science, Statistics, Business Analytics, or related field

  • 5+ years of experience in data analytics with exposure to commerce, payments, or subscription businesses

  • Proficiency with Apache Spark and Databricks for large-scale data transformation, analysis, and developing production-ready notebook workflows

  • Strong proficiency in SQL and data analysis programming languages (Python, R)

  • Experience with business intelligence and visualization tools (AWS Quicksight, Tableau, Power BI, Looker)

  • Knowledge of financial metrics, payment KPIs, and risk indicators

  • Experience translating complex transactional data into business insights

  • Background in developing dashboards for different audiences with varying technical sophistication

  • Understanding of fraud analytics, payment performance metrics, or revenue optimization techniques

Ways to stand out from the crowd:

  • Master's degree in a relevant field

  • Experience with commerce platforms, payment processors, or fraud prevention systems

  • Background in marketing analytics, customer segmentation, or lifetime value modeling

  • Knowledge of machine learning techniques for anomaly detection or predictive analytics

  • Experience with financial reporting, revenue forecasting, or business performance analysis

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Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.

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