עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!

במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.

מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.

חברה בתחום כללי

תל אביב - יפו

חברה בתחום כללי

תל אביב - יפו
מלאה
18,000-26,000 ₪ (הערכה מבוססת AI)
הערכה מבוססת AI ולא שכר של המעסיק

we are looking for a Analytics Engineer.
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

This position is open to all candidates.