Analytics Engineer

As an Analytics Engineer within the Delivery & Operations organization, you will operate at the intersection of data engineering and analytics, with a strong focus on data quality, reliability, and scalability.This role combines hands-on ownership of data pipelines and data transformations with the ability to effectively interface with customer-facing teams (Customer Success, and Delivery) when needed-helping ensure that data outputs are accurate, clear, and aligned with real-world use cases.ResponsibilitiesBuild and maintain data models, pipelines, and ETL processes to support analytics, reporting, and machine learning.Own data quality and validation, including monitoring, auditing datasets, and identifying anomalies.Support customer-facing teams by providing reliable data, clarifying definitions, and investigating data issues.Collaborate cross-functionally and work with existing codebases to debug, improve, and maintain data workflows.Ensure high-quality data across the lifecycle to support reliable ML pipelines.Requirements: Requirements3 years of experience in Python development (production-level data logic, not just scripting).2 years of experience with data validation / data quality practices.Experience with data pipelines / ETL processes.Proficiency in Pandas (or similar libraries).Strong SQL and database knowledge.Experience working with existing production codebases (debugging, refactoring).Ability to communicate clearly with non-technical stakeholders when needed.Strong analytical thinking and problem-solving skills.High attention to detail and commitment to data accuracy.Bachelors degree in Computer Science, Statistics, Industrial Engineering, or a related quantitative field.‍AdvantagesFamiliarity with data modeling best practices.Experience supporting customer-facing data use cases or deliverables.Background in DataOps / data reliability practices.Exposure to machine learning pipelines.This position is open to all candidates.