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
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.
Bachelors degree in Computer Science, Statistics, Industrial Engineering, or a related quantitative field.
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.
