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.
Responsibilities:
Build 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: Requirements:
3+ 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.
Advantages:
Familiarity 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.