Data Analytics Engineer

Details

We're looking for candidates with a stronger analytics and product mindset than a traditional Data Engineering profile. 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, the focus is on deriving insights, defining meaningful metrics, solving business problems through data, and manipulating and processing large datasets in a rigorous and scalable way.

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 tested on both their technical data modeling acumen and business orientation.

Role Overview

As a Data Analytics Engineer, you will design and develop scalable analytical solutions that support GenAI application teams across search and discovery, AI companions and 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 and 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, and Insights

• Transform large and complex datasets into actionable insights for operational, historical, and predictive analysis.
• Build reusable analytical datasets enabling self-service 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.

Requirements

Experience and 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 production environments and delivering timely, 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.
• Experience working 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 and 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.