Key Skills

Job Description

We're looking for candidates with a stronger analytics and product mindset than a traditional Data Engineering profile. As such, former Senior Data Engineers or Data Architects whose experience is primarily focused on infrastructure and platform development are unlikely to be a strong fit. The role is not centered on building data infrastructure from scratch. Instead, we're looking for individuals who can derive insights, define meaningful metrics, solve business problems through data, and manipulate and process large datasets in a rigorous and scalable way to serve those purposes. Ideal candidates have owned analytical domains end-to-end, including data modeling, data quality, experimentation analysis, dashboarding, monitoring, and stakeholder-facing analytics. The target profile is closer to an Analytics Engineer or highly technical Analyst than a Data Engineer: roughly two-thirds analytics, product thinking, and business problem-solving, and one-third data engineering and data modeling. Candidates will be indeed tested on both their technical data modeling acumen as well as business orientation. Role Overview As a Data Analytics Engineer, you will design and develop scalable analytical solutions that support GenAI application teams across search & discovery, AI companions/chatbots, customer support AI applications, and LLM evaluation workflows. This is a highly business-oriented role focused on bridging the gap between raw data and application-specific analytical needs. You will own data domains end-to-end, ensure data quality and usability, and transform complex datasets into actionable insights, monitoring systems, and evaluation frameworks. This is not a traditional Data Engineer role. The focus is on application logic, analytics, data modeling, quality, experimentation, and insight generation - rather than infrastructure ownership or EL platform engineering. Responsibilities Data Modeling & Ownership Own business and application data domains end-to-end. Ensure correctness, quality, and consistency of analytical datasets and logging. Design and maintain scalable analytical data models and reusable data products. Perform data governance responsibilities, including data classification, stewardship, quality monitoring, compliance, and security considerations. Maintain and improve data pipeline health through monitoring, troubleshooting, performance tuning, and proactive risk mitigation. Analytics, Monitoring & Insights Transform large and complex datasets into actionable insights for operational, historical, and predictive analysis. Build reusable analytical datasets enabling self-serve analytics across teams. Develop application-specific monitoring tables, quality dashboards, and cost dashboards. Analyze experiments, model behavior, and LLM evaluation results. Validate data and GenAI products through exploratory analysis and visualizations before release. Partner with product managers, scientists, and engineers to identify analytical opportunities and define analytics roadmaps. Qualifications & Skills Experience & Mindset 3+ years of experience in analytics, data, or software-adjacent roles working with large-scale data systems. Strong business orientation and customer-focused mindset. Ability to independently navigate ambiguity, prioritize by impact, and drive initiatives end-to-end. Strong analytical thinking with the ability to derive insights beyond surface-level metrics. Experience contributing to the production environment, delivering timely and high-impact insights to various stakeholders. Experience in ML/AI product environments, evaluation workflows, or model/error analysis is a strong advantage. Resourceful and thoughtful use of AI tools and assistants. Technical Skills Strong SQL skills and experience working with relational databases in analytical environments. Hands-on experience with Python and PySpark. Strong experience with data modeling and modern Data Warehouse practices. Experience building maintainable, reusable, production-grade analytical code and transformations. Work extensively with free text, unstructured data, LLM-generated outputs, and AI application telemetry. Advantage: Experience with dbt, Snowflake, Streamlit, Airflow, or Argo. Advantage: Experience working with AI/LLM-related datasets and evaluation pipelines. Collaboration & Communication Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders. Proven ability to collaborate across product, engineering, analytics, and data science teams. Self-driven, proactive, and comfortable owning ambiguous problem spaces independently.

Core Responsibilities

Own application and business data domains end-to-end, building scalable analytical models and reusable datasets while maintaining data quality, governance, and pipeline health. Turn complex data into insights, dashboards, monitoring systems, and evaluation frameworks, partnering with product, engineering, and data science teams.

Requirements

Candidates need at least three years of experience in analytics, data, or software-adjacent roles, strong business orientation, and the ability to independently deliver analytical work. Strong SQL, Python, PySpark, data modeling, and production-grade analytical coding experience are required; experience with AI/ML products and tools such as dbt, Snowflake, Streamlit, Airflow, or Argo is advantageous.