Data Platform Engineer

Personetics

full-time

Posted on: 9/19/2025

Origin: • 🇮🇱 Israel

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Mid-LevelSenior

AirflowAWSAzureCloudETLGoogle Cloud PlatformKafkaPythonSparkSQL

About the role

Design, build and scale the BI data analytics platform from the ground up.
Build foundations enabling scalable data pipelines and robust data modeling for real-time and batch analytics, ML models and business insights.
Take full ownership of design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability and security.
Lead the design and architecture of the data platform from integration to transformation, modeling, storage and access.
Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting and product integrations.
Establish and enforce best practices for data quality, lineage, observability and governance to ensure accuracy and consistency.
Integrate modern tools and frameworks such as Airflow, dbt, Databricks, Power BI and streaming platforms.
Collaborate cross-functionally with product, engineering and analytics teams to translate business needs into data infrastructure.
Promote a data-driven culture and empower stakeholders with reliable self-service data access.

5+ years of hands-on experience in data engineering and in building data products for analytics and business intelligence.
Proven track record of designing and implementing large-scale data platforms or ETL architectures from the ground up.
Strong hands-on experience with ETL tools and data Warehouse/Lakehouse products (Airflow, Airbyte, dbt, Databricks).
Experience supporting both batch pipelines and real-time streaming architectures (e.g., Kafka, Spark Streaming).
Proficiency in Python, SQL, and cloud data engineering environments (AWS, Azure, or GCP).
Familiarity with data visualization tools like Power BI, Looker, or similar.
BSc in Computer Science or a related field from a leading university.
Nice to have: experience working in early-stage projects, building data systems from scratch.
Nice to have: background in building operational analytics pipelines in which analytical data feeds real-time product business logic.
Nice to have: hands-on experience with ML model training pipelines.
Nice to have: experience in cost optimization in modern cloud environments.
Nice to have: knowledge of data governance principles, compliance, and security best practices.