Analytics Engineer

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) - 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