Job description
Description
About the Position
At Harmonya, we’re building cutting-edge AI solutions powered by high-quality, reliable data. 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) - must!
2+ years of experience with data validation / data quality practices - must!
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
Bachelor’s 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