Analytics Engineer (Temporary Maternity Leave Replacement)
Location: Tel Aviv, Israel
Experience Level: Intermediate
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)
- 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
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